{"id":1628,"date":"2025-08-03T02:33:41","date_gmt":"2025-08-02T17:33:41","guid":{"rendered":"https:\/\/www.jsbi.org\/iibmp2025\/?page_id=1628"},"modified":"2025-09-03T16:00:31","modified_gmt":"2025-09-03T07:00:31","slug":"%e3%83%9d%e3%82%b9%e3%82%bf%e3%83%bc%e7%99%ba%e8%a1%a8","status":"publish","type":"page","link":"https:\/\/www.jsbi.org\/iibmp2025\/%e3%83%9d%e3%82%b9%e3%82%bf%e3%83%bc%e7%99%ba%e8%a1%a8\/","title":{"rendered":"\u30dd\u30b9\u30bf\u30fc\u767a\u8868"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u8005\u3078\u306e\u3054\u6848\u5185<\/h2>\n\n\n\n<p><strong>\u30dd\u30b9\u30bf\u30fc\u8cbc\u4ed8\u53ef\u80fd\u671f\u9593\uff1a<\/strong><br>\u30dd\u30b9\u30bf\u30fc\u306f9\u67083\u65e5\uff08\u6c34\uff099:30\u301c\u3001\u307e\u305f\u306f\u30019\u67084\u65e5\uff08\u6728\uff098:30\u301c\u306b\u6c7a\u3081\u3089\u308c\u305f\u30dd\u30b9\u30bf\u30fc\u756a\u53f7\u306e\u30dc\u30fc\u30c9\u306b\u63b2\u793a\u3057\u30019\u67085\u65e5\uff08\u6728\uff0913:00\u9803\u307e\u3067\u306b\u64a4\u53ce\u3092\u304a\u9858\u3044\u3044\u305f\u3057\u307e\u3059\u3002<\/p>\n\n\n\n<p><strong>\u30dd\u30b9\u30bf\u30fc\u767a\u8868\uff1a<\/strong><br>\u3000\u25cb\u5947\u6570\u756a\u53f7\u30009\u67083\u65e5\uff08\u6c34\uff0911\uff1a30-12\uff1a20<br>\u3000\u3000\u3000\u3000\u3000\u3000\u30009\u67084\u65e5\uff08\u6728\uff0917\uff1a20-18\uff1a10<br>\u3000\u25cb\u5076\u6570\u756a\u53f7\u3000 9\u67083\u65e5\uff08\u6c34\uff0917\uff1a00-17\uff1a50<br>\u3000\u3000\u3000\u3000\u3000\u3000\u30009\u67084\u65e5\uff08\u6728\uff0911\uff1a50-12\uff1a40<br>\u203b 9\u67083\u65e5\u30684\u65e5\u3067\u30dd\u30b9\u30bf\u30fc\u306e\u5165\u308c\u66ff\u3048\u306f\u884c\u3044\u307e\u305b\u3093\u3002\u3054\u4e88\u5b9a\u304c\u5408\u308f\u306a\u3044\u65b9\u306f\u3001\u5c11\u306a\u304f\u3068\u30823\u65e5\u3082\u3057\u304f\u306f4\u65e5\u306e\u3069\u3061\u3089\u304b\u3067\u30d7\u30ec\u30bc\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u3092\u304a\u9858\u3044\u3044\u305f\u3057\u307e\u3059\u3002<\/p>\n\n\n\n<ul class=\"wp-block-list is-style-vk-check-mark\">\n<li>\u884c\u52d5\u898f\u7bc4\u3092\u9075\u5b88\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/li>\n\n\n\n<li>\u30dd\u30b9\u30bf\u30fc\u30dc\u30fc\u30c9\u306e\u30b5\u30a4\u30ba\u306f W1200mm \u00d7 H1800mm \u3067\u3059\u3002\u30dd\u30b9\u30bf\u30fc\u756a\u53f7\u306f\u4e0a\u7aef\u304b\u3089\u7d04100mm\u306e\u9593\u306b\u63b2\u793a\u3044\u305f\u3057\u307e\u3059\u3002<\/li>\n\n\n\n<li>\u30dc\u30fc\u30c9\u306b\u306f\u30d4\u30f3\u304c\u4f7f\u7528\u3067\u304d\u307e\u305b\u3093\u3002\u5e03\u88fd\u30dd\u30b9\u30bf\u30fc\u306f\u907f\u3051\u3066\u3044\u305f\u3060\u304d\u3001\u4e8b\u52d9\u5c40\u3067\u7528\u610f\u3059\u308b\u4e21\u9762\u30c6\u30fc\u30d7\u3092\u3054\u4f7f\u7528\u304f\u3060\u3055\u3044\u3002<\/li>\n\n\n\n<li>\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u6642\u9593\u4e2d\u3001\u767a\u8868\u8005\u306f\u4f1a\u5834\u5185\u306b\u7f6e\u3044\u3066\u3042\u308b\u30ea\u30dc\u30f3(\u7dd1\u8272)\u3092\u8eab\u306b\u3064\u3051\u3066\u304f\u3060\u3055\u3044\u3002<\/li>\n\n\n\n<li>\u767a\u8868\u8005\u306e\u4e2d\u304b\u3089\u300c\u5f8c\u85e4\u4fee\u8cde\uff08\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u90e8\u9580\uff09\u300d\uff081\u540d\uff09\u304a\u3088\u3073\u300c\u512a\u79c0\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u8cde\u300d\uff08\u6570\u540d\uff09\u3092\u9078\u51fa\u3044\u305f\u3057\u307e\u3059\u3002<\/li>\n\n\n\n<li>\u30dd\u30b9\u30bf\u30fc\u8cde\u306e\u9078\u8003\u306f\u3001\u53c2\u52a0\u8005\u306e\u6295\u7968\u6570\u306b\u57fa\u3065\u3044\u3066\u3001\u7406\u4e8b\u9577\u30fb\u526f\u7406\u4e8b\u9577\u30fb\u5927\u4f1a\u9577\u30fb\u30d7\u30ed\u30b0\u30e9\u30e0\u59d4\u54e1\u9577\u304b\u3089\u306a\u308b\u9078\u8003\u59d4\u54e1\u4f1a\u304c\u884c\u3044\u307e\u3059\u3002<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">\u4e00\u89a7<\/h2>\n\n\n\n<p><strong>PO-001<br><\/strong>Reinforcement Learning with Masked Language Model for CAR-T Cell Therapy Application<br>\u30de\u30b9\u30af\u8a00\u8a9e\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u5f37\u5316\u5b66\u7fd2\u6cd5\u306e\u958b\u767a\u3068CAR-T\u7d30\u80de\u7642\u6cd5\u3078\u306e\u5fdc\u7528<br>\u7530\u4e2d \u512a\u6b21<sup>1<\/sup>, \u9ad8\u702c \u8ad2\u4e00<sup>1<\/sup>, \u5211\u90e8 \u597d\u5f18<sup>1<\/sup>, \u4e95\u5cf6 \u5927\u5f25<sup>1<\/sup>, \u6dfa\u539f \u5f70\u898f<sup>1<\/sup>, \u4e45\u7530 \u6607\u4e8c<sup>1<\/sup>, \u5409\u7530 \u5553<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u682a\u5f0f\u4f1a\u793e \u65e5\u7acb\u88fd\u4f5c\u6240)<\/p>\n\n\n\n<p><strong>PO-002<br><\/strong>Mix-Geneformer : Unified Representation Learning for Human and Mouse scRNA-seq Data<br>Mix-Geneformer : \u30d2\u30c8\u30fb\u30de\u30a6\u30b9scRNA-seq\u306e\u7d71\u4e00\u8868\u73fe\u5b66\u7fd2\u30e2\u30c7\u30eb<br>\u897f\u5c3e \u512a\u5e0c<sup>1<\/sup>, \u5c71\u4e0b \u9686\u7fa9<sup>1<\/sup>, \u4f0a\u85e4 \u5553\u592a<sup>1<\/sup>, \u5e73\u5ddd \u7ffc<sup>1<\/sup>, \u85e4\u5409 \u5f18\u4e98<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u4e2d\u90e8\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-003<br><\/strong>Enhancing CAR-T cell activity prediction via fine-tuning protein language models with generated CAR sequences<br>\u751f\u6210CAR\u914d\u5217\u3092\u7528\u3044\u305f\u30bf\u30f3\u30d1\u30af\u8cea\u8a00\u8a9e\u30e2\u30c7\u30eb\u306e\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u306b\u3088\u308bCAR-T\u7d30\u80de\u6a5f\u80fd\u4e88\u6e2c\u6027\u80fd\u306e\u5411\u4e0a<br>\u5409\u7530 \u5553<sup>1<\/sup>, \u4e45\u7530 \u6607\u4e8c<sup>1<\/sup>, \u9ad8\u702c \u8ad2\u4e00<sup>1<\/sup>, \u5927\u718a \u6566\u53f2<sup>1<\/sup>, \u77f3\u7530 \u7fa9\u4eba<sup>1<\/sup>, \u5ddd\u826f \u6bc5\u4eba<sup>1<\/sup>, \u5c71\u4e0b \u62d3\u4e5f<sup>1<\/sup>, \u4f0a\u85e4 \u5927\u4ecb<sup>1<\/sup>, \u5965\u7530 \u667a\u5f66<sup>1<\/sup>, \u534a\u6fa4 \u5b8f\u5b50<sup>1<\/sup>, \u6b66\u7530 \u5fd7\u6d25<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u682a\u5f0f\u4f1a\u793e\u65e5\u7acb\u88fd\u4f5c\u6240)<\/p>\n\n\n\n<p><strong>PO-004<br><\/strong>Zero-Shot Extraction of Experimental Protocols from Scientific Papers Using Large Language Models<br>\u5927\u898f\u6a21\u8a00\u8a9e\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u79d1\u5b66\u8ad6\u6587\u304b\u3089\u306e\u5b9f\u9a13\u30d7\u30ed\u30c8\u30b3\u30eb\u306e\u30bc\u30ed\u30b7\u30e7\u30c3\u30c8\u62bd\u51fa<br>\u8305\u539f \u6dbc\u5e73<sup>1<\/sup>, \u5c0f\u6c60 \u822a\u5e73<sup>1<\/sup>, \u661f\u91ce \u5321\u88d5<sup>1<\/sup>, \u99d2\u6fa4 \u6709\u91cc<sup>1<\/sup>, \u5c0f\u677e \u76f4\u4eba<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u682a\u5f0f\u4f1a\u793e\u30e1\u30d3\u30a6\u30b9)<\/p>\n\n\n\n<p><strong>PO-005<br><\/strong>Machine Learning-Based Prediction of Optimal Chaperones for Protein Solubility in Cell-Free Expression Systems<br>\u7121\u7d30\u80de\u767a\u73fe\u7cfb\u306b\u304a\u3051\u308b\u30bf\u30f3\u30d1\u30af\u8cea\u53ef\u6eb6\u5316\u306e\u305f\u3081\u306e\u6700\u9069\u30b7\u30e3\u30da\u30ed\u30f3\u4e88\u6e2c\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9<br>\u5b89\u85e4 \u4ec1\u52d9<sup>1<\/sup>, \u5009\u7530 \u535a\u4e4b<sup>2<\/sup>, \u524d\u7530 \u548c\u52f2<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66\u60c5\u5831\u5de5\u5b66\u90e8\u751f\u547d\u5316\u5b66\u60c5\u5831\u5de5\u5b66\u79d1\u524d\u7530\u7814\u7a76\u5ba4, <sup>2<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66\u60c5\u5831\u5de5\u5b66\u90e8\u751f\u547d\u5316\u5b66\u60c5\u5831\u5de5\u5b66\u79d1\u5009\u7530\u7814\u7a76\u5ba4)<\/p>\n\n\n\n<p><strong>PO-006<br><\/strong>Deep-Learning-Based Generation of Protein-Binding RNA Sequences<br>\u6df1\u5c64\u5b66\u7fd2\u3092\u7528\u3044\u305f\u30bf\u30f3\u30d1\u30af\u8cea\u306b\u7d50\u5408\u3059\u308bRNA\u914d\u5217\u306e\u8a2d\u8a08<br>\u77f3\u4e95 \u6b69\u4f73<sup>1<\/sup>, \u79cb\u5c71 \u771f\u90a3\u6597<sup>2<\/sup>, \u698a\u539f \u5eb7\u6587<sup>2<\/sup>&nbsp; (<sup>1<\/sup>\u6176\u61c9\u7fa9\u587e\u5927\u5b66, <sup>2<\/sup>\u5317\u91cc\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-007<br><\/strong>Generation of Mutant Amino Acid Sequences for Highly Functional Proteins with a Variational Autoencoder Trained on Sequence-Function Relationships<br>\u914d\u5217-\u6a5f\u80fd\u95a2\u4fc2\u3092\u5b66\u7fd2\u3057\u305fVariational Autoencoder\u306b\u3088\u308b\u9ad8\u6a5f\u80fd\u5909\u7570\u30bf\u30f3\u30d1\u30af\u8cea\u30a2\u30df\u30ce\u9178\u914d\u5217\u306e\u751f\u6210<br>\u4e95\u5cf6 \u5927\u5f25<sup>1<\/sup>, \u5211\u90e8 \u597d\u5f18<sup>1<\/sup>, \u9ad8\u702c \u8ad2\u4e00<sup>1<\/sup>, \u5c0f\u5c71 \u5149<sup>1<\/sup>, \u6dfa\u539f \u5f70\u898f<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u682a\u5f0f\u4f1a\u793e\u65e5\u7acb\u88fd\u4f5c\u6240)<\/p>\n\n\n\n<p><strong>PO-008<br><\/strong>Developing Embedding Models for Cytochrome P450\u2013Ligand Interaction Scoring<br>\u30b7\u30c8\u30af\u30ed\u30e0P450\u3068\u30ea\u30ac\u30f3\u30c9\u306e\u76f8\u4e92\u4f5c\u7528\u30b9\u30b3\u30a2\u30ea\u30f3\u30b0\u306e\u305f\u3081\u306e\u57cb\u3081\u8fbc\u307f\u30e2\u30c7\u30eb\u306e\u958b\u767a<br>MAR TIN<sup>1<\/sup>, Takigawa Ichigaku<sup>1<\/sup>&nbsp; (<sup>1<\/sup>WPI-ICReDD, Hokkaido University)<\/p>\n\n\n\n<p><strong>PO-009<br><\/strong>Data-Driven Drug Discovery Using Graph Neural Networks: A Multimodal Approach to COVID-19 Therapeutic Candidates<br>Graph Neural Network\u3092\u6d3b\u7528\u3057\u305f\u30c7\u30fc\u30bf\u30c9\u30ea\u30d6\u30f3\u5275\u85ac\uff1a\u591a\u5c64\u7684\u30a2\u30d7\u30ed\u30fc\u30c1\u306b\u3088\u308bCOVID-19\u6cbb\u7642\u85ac\u5019\u88dc\u306e\u63a2\u7d22<br>\u4e2d\u5c71 \u88d5\u4ecb<sup>1<\/sup>, \u8fbb \u771f\u543e<sup>2<\/sup>, \u7d30\u898b \u5149\u4e00<sup>3<\/sup>, \u5c71\u672c \u7d05\u53f8<sup>4<\/sup>, \u52a0\u85e4 \u73e0\u862d<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u682a\u5f0f\u4f1a\u793e\u30b8\u30a7\u30af\u30b9\u30f4\u30a1\u30eb, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66\u5148\u7aef\u79d1\u5b66\u6280\u8853\u7814\u7a76\u30bb\u30f3\u30bf\u30fc, <sup>3<\/sup>\u8fd1\u757f\u5927\u5b66\u85ac\u5b66\u90e8, 4\u72ec\u7acb\u7814\u7a76\u8005)<\/p>\n\n\n\n<p><strong>PO-010<br><\/strong>Development of a Machine Learning\u2013Based Platform for Predicting Developmental Toxicity Using Human iPSC Transcriptomics<br>iPS\u7d30\u80de\u3092\u7528\u3044\u305f\u74b0\u5883\u5316\u5b66\u7269\u8cea\u306e\u767a\u9054\u6bd2\u6027\u8a55\u4fa1\u5411\u3051\u6a5f\u68b0\u5b66\u7fd2\u6700\u9069\u5316<br>\u66fd\u6839 \u79c0\u5b50<sup>1<\/sup>, \u672c\u5143 \u6052\u8d8a<sup>1<\/sup>, \u6a4b\u722a \u7f8e\u840c<sup>1<\/sup>, \u9f4b\u85e4 \u5f69\u6597<sup>2<\/sup>, \u7389\u7530 \u5609\u7d00<sup>3<\/sup>, \u52a0\u85e4 \u6bc5<sup>2<\/sup>&nbsp; (<sup>1<\/sup>\u6a2a\u6d5c\u85ac\u79d1\u5927\u5b66, <sup>2<\/sup>\u7fa4\u99ac\u5927\u5b66, <sup>3<\/sup>\u5f18\u524d\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-011<br><\/strong>A multi-step deep learning-based framework for accurate prediction of multi-drug anticancer efficacy<br>\u6297\u304c\u3093\u5264\u306e\u591a\u5264\u4f75\u7528\u52b9\u679c\u4e88\u6e2c\u306e\u305f\u3081\u306e\u591a\u6bb5\u968e\u6df1\u5c64\u5b66\u7fd2\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af<br>\u5927\u7530 \u822a\u5e73<sup>1,2<\/sup>, \u53e4\u8cc0 \u5927\u4ecb<sup>5<\/sup>, \u9ebb\u751f \u5553\u6587<sup>3,4<\/sup>, \u6e05\u6c34 \u79c0\u5e78<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66\u5927\u5b66\u9662\u3000\u533b\u6b6f\u5b66\u7dcf\u5408\u7814\u7a76\u79d1, <sup>2<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66\u3000\u7dcf\u5408\u7814\u7a76\u9662\u3000M\uff06D\u30c7\u30fc\u30bf\u79d1\u5b66\u30bb\u30f3\u30bf\u30fc\u3000AI\u30b7\u30b9\u30c6\u30e0\u533b\u79d1\u5b66\u5206\u91ce, <sup>3<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66\u3000\u7dcf\u5408\u7814\u7a76\u9662, <sup>4<\/sup>\u30ab\u30ed\u30ea\u30f3\u30b9\u30ab\u7814\u7a76\u6240\u3000\u533b\u5b66\u90e8, 5\u4f50\u8cc0\u5927\u5b66\u533b\u5b66\u90e8\u9644\u5c5e\u75c5\u533b\u9662\u3000\u533b\u7642\u7814\u4fee\u30bb\u30f3\u30bf\u30fc )<\/p>\n\n\n\n<p><strong>PO-012<br><\/strong>The development of a host prediction model for coronavirus using DNABERT-based AI<br>DNABERT\u30d9\u30fc\u30b9AI\u306b\u3088\u308b\u30b3\u30ed\u30ca\u30a6\u30a4\u30eb\u30b9\u306e\u5bbf\u4e3b\u4e88\u6e2c\u30e2\u30c7\u30eb\u306e\u958b\u767a<br>\u5c71\u672c \u660c\u4ec1<sup>1<\/sup>, \u5869\u751f \u771f\u53f2<sup>1<\/sup>, \u5ca9\ufa11 \u7950\u8cb4<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u9577\u6d5c\u30d0\u30a4\u30aa\u5927\u5b66\u5927\u5b66\u9662\u30d0\u30a4\u30aa\u30b5\u30a4\u30a8\u30f3\u30b9\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-013<br><\/strong>Domain-Adaptive Graph Autoencoding for Cross-Tissue Bulk Transcriptome Deconvolution<br>\u30c9\u30e1\u30a4\u30f3\u9069\u5fdc\u578b\u30b0\u30e9\u30d5\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0\u3092\u7528\u3044\u305f\u7d44\u7e54\u6a2a\u65ad\u30c7\u30b3\u30f3\u30dc\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3<br>\u6771 \u4e00\u7e54<sup>1<\/sup>, \u6c34\u91ce \u5fe0\u5feb<sup>1<\/sup>, \u6960\u539f \u6d0b\u4e4b<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662\u85ac\u5b66\u7cfb\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-014<br><\/strong>Design of localization control sequences based on prediction of subcellular localization of RNA using deep learning<br>\u6df1\u5c64\u5b66\u7fd2\u306b\u3088\u308bRNA\u306e\u7d30\u80de\u5185\u5c40\u5728\u4e88\u6e2c\u306b\u57fa\u3065\u304f\u5c40\u5728\u5236\u5fa1\u914d\u5217\u306e\u8a2d\u8a08<br>\u4e2d\u82b1\u7530 \u77e5\u91cc<sup>1<\/sup>, \u698a\u539f \u5eb7\u6587<sup>2<\/sup>, \u79cb\u5c71 \u771f\u90a3\u6597<sup>2<\/sup>&nbsp; (<sup>1<\/sup>\u6176\u61c9\u7fa9\u587e\u5927\u5b66\u5927\u5b66\u9662, <sup>2<\/sup>\u5317\u91cc\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-015<br><\/strong>Evaluation and Applicability of Large-scale Vision-Language Models in the Japanese National Examination for Clinical Laboratory Technicians<br>\u81e8\u5e8a\u691c\u67fb\u6280\u5e2b\u56fd\u5bb6\u8a66\u9a13\u306b\u304a\u3051\u308b\u5927\u898f\u6a21\u8a00\u8a9e\u30e2\u30c7\u30eb\u306e\u6027\u80fd\u8a55\u4fa1\u3068\u5fdc\u7528\u53ef\u80fd\u6027\u306e\u691c\u8a0e<br>\u6751\u4e0a \u822a\u6c70<sup>1<\/sup>, \u5c3e\u5d0e \u907c<sup>1,2<\/sup>, \u677e\u6fa4 \u4eae\u8f14<sup>1<\/sup>, \u7530\u539f-\u65b0\u4e95 \u60a0\u4e5f<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u7b51\u6ce2\u5927\u5b66, <sup>2<\/sup>\u7406\u5316\u5b66\u7814\u7a76\u6240)<\/p>\n\n\n\n<p><strong>PO-016<br><\/strong>Decoding scFv\u2013Antigen Interactions Using Language Models<br>\u8a00\u8a9e\u30e2\u30c7\u30eb\u306b\u3088\u308bscFv-\u6297\u539f\u76f8\u4e92\u4f5c\u7528\u306e\u89e3\u8aad<br>\u85e4\u539f \u5d69\u58eb<sup>1<\/sup>, \u6e05\u6c34 \u79c0\u5e78<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-017<br><\/strong>Automating scRNA\u2011seq data analysis with MCP servers and LLM agents<br>MCP\u30b5\u30fc\u30d0\u30fc\u3068LLM\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u3092\u6d3b\u7528\u3057\u305fscRNA-seq\u30c7\u30fc\u30bf\u89e3\u6790\u306e\u81ea\u52d5\u5316<br>\u65e9\u5ddd \u6176\u7d00<sup>1<\/sup>, \u5c3e\u5d0e \u907c<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u7b51\u6ce2\u5927\u5b66, <sup>2<\/sup>\u7406\u7814)<\/p>\n\n\n\n<p><strong>PO-018<br><\/strong>MOGEDN: Few-Shot Multi-Omics Disease-Subtype Classification via a Graph Convolutional Encoder\u2013Decoder for Missing-View Recovery and Biomarker Discovery<br>MOGEDN\uff1a\u6b20\u640d\u30d3\u30e5\u30fc\u88dc\u5b8c\u3068\u30d0\u30a4\u30aa\u30de\u30fc\u30ab\u30fc\u540c\u5b9a\u306e\u305f\u3081\u306e\u30b0\u30e9\u30d5\u7573\u307f\u8fbc\u307f\u30a8\u30f3\u30b3\u30fc\u30c0\u30fb\u30c7\u30b3\u30fc\u30c0\u306b\u3088\u308b\u5c11\u6570\u30b7\u30e7\u30c3\u30c8\u591a\u5c64\u30aa\u30df\u30c3\u30af\u30b9\u75be\u60a3\u30b5\u30d6\u30bf\u30a4\u30d7\u5206\u985e<br>JIN Dingnan<sup>3<\/sup>, \u9f4b\u85e4 \u88d5<sup>1,2,3<\/sup>&nbsp; (<sup>1<\/sup>School of Frontier Engineering, Kitasato University, <sup>2<\/sup>National Institute of Advanced Industrial Science and Technology, <sup>3<\/sup>Graduate School of Frontier Sciences, The University of Tokyo)<\/p>\n\n\n\n<p><strong>PO-019<\/strong> \/ HT-202<strong><br><\/strong>Data-efficient protein mutational effect prediction with weak supervision by molecular simulation and protein language models<br>\u5206\u5b50\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3068\u30bf\u30f3\u30d1\u30af\u8cea\u8a00\u8a9e\u30e2\u30c7\u30eb\u306b\u3088\u308b\u5f31\u6559\u5e2b\u3042\u308a\u5b66\u7fd2\u3092\u7528\u3044\u305f\u30c7\u30fc\u30bf\u52b9\u7387\u7684\u306a\u30bf\u30f3\u30d1\u30af\u8cea\u5909\u7570\u52b9\u679c\u4e88\u6e2c<br>\u51fa\u53e3 \u9244\u5e73<sup>1,2<\/sup>, \u6765\u898b\u7530 \u9065\u4e00<sup>3<\/sup>, \u98ef\u7530 \u614e\u4ec1<sup>3<\/sup>, \u5c0f\u6797 \u6d77\u6e21<sup>2<\/sup>, \u9f4b\u85e4 \u88d5<sup>1,2,3<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66, <sup>2<\/sup>\u7523\u696d\u6280\u8853\u7dcf\u5408\u7814\u7a76\u6240, <sup>3<\/sup>\u5317\u91cc\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-020<br><\/strong>Machine learning-guided discovery and validation of translation-enhancing peptides in Escherichia coli<br>\u6a5f\u68b0\u5b66\u7fd2\u3068\u5b9f\u9a13\u306b\u3088\u308b\u30bf\u30f3\u30d1\u30af\u8cea\u306e\u7ffb\u8a33\u4fc3\u9032\u914d\u5217\u306e\u63a2\u7d22\u3068\u691c\u8a3c<br>\u672c\u91ce \u5343\u6075<sup>1,2<\/sup>, \u6a2a\u5c71 \u6e90\u592a\u6717<sup>1,3<\/sup>, \u4e2d\u91ce \u79c0\u96c4<sup>4<\/sup>, \u6d5c\u7530 \u9053\u662d<sup>1,3<\/sup>, \u52a0\u85e4 \u6643\u4ee3<sup>4<\/sup>&nbsp; (<sup>1<\/sup>\u7523\u7dcf\u7814\u3000\u7d30\u80de\u5206\u5b50\u5de5\u5b66,<sup> 2<\/sup>\u7523\u7dcf\u7814\u3000\u30bb\u30eb\u30d5\u30b1\u30a2\u5b9f\u88c5\u7814\u7a76\u30bb\u30f3\u30bf\u30fc, <sup>3<\/sup>\u65e9\u5927\u3000\u7406\u5de5\u5b66\u8853\u9662, <sup>4<\/sup>\u540d\u5927\u9662\u3000\u751f\u547d\u8fb2\u5b66\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-021<br><\/strong>Anomaly Detection in C. elegans embryos using Progressive Learning of Memory-Augmented Autoencoders<br>\u30e1\u30e2\u30ea\u30e2\u30b8\u30e5\u30fc\u30eb\u4ed8\u304d\u81ea\u5df1\u7b26\u53f7\u5316\u5668\u306e\u6f38\u9032\u5b66\u7fd2\u306b\u3088\u308b\u7dda\u866b\u80da\u306e\u7570\u5e38\u691c\u77e5<br>\u7530\u4e2d \u7701\u543e<sup>1<\/sup>, \u9060\u91cc \u7531\u4f73\u5b50<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u7acb\u547d\u9928\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-022<br><\/strong>Robust Segmentation of C. elegans DIC Images Using Deep Learning under Varying Imaging Conditions<br>\u64ae\u5f71\u6761\u4ef6\u306e\u3070\u3089\u3064\u304d\u306b\u9811\u5065\u306a\u6df1\u5c64\u5b66\u7fd2\u306b\u3088\u308b\u7dda\u866bDIC\u753b\u50cf\u306e\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3<br>\u58ec\u751f \u5927\u548c<sup>1<\/sup>, \u9060\u91cc \u7531\u4f73\u5b50<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u7acb\u547d\u9928\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-023<br><\/strong>Improvement of RNA Secondary Structure Prediction Models\u00a0 Utilizing Chemical Mapping Data<br>\u30b1\u30df\u30ab\u30eb\u30de\u30c3\u30d4\u30f3\u30b0\u30c7\u30fc\u30bf\u3092\u6d3b\u7528\u3057\u305fRNA\u4e8c\u6b21\u69cb\u9020\u4e88\u6e2c\u30e2\u30c7\u30eb\u306e\u7cbe\u5ea6\u5411\u4e0a<br>\u5c71\u5185 \u51dc\u592a\u90ce<sup>1<\/sup>, \u7bc9\u5c71 \u7fd4<sup>1<\/sup>, \u4f50\u85e4 \u5065\u543e<sup>1<\/sup>\u00a0 (<sup>1<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-024<br><\/strong>Benchmarking Deep Learning Approaches for Predicting Protein-Ligand Interactions to Identify Therapeutic Inhibitors of AKR1B10<br>AKR1B10\u963b\u5bb3\u5264\u958b\u767a\u306b\u5411\u3051\u305f\u30bf\u30f3\u30d1\u30af\u8cea\u2015\u57fa\u8cea\u76f8\u4e92\u4f5c\u7528\u4e88\u6e2c\u6cd5\u306e\u6027\u80fd\u8a55\u4fa1<br>\u6cb3\u91ce \u771f\u4e5f<sup>1,2<\/sup>, \u4e94\u5341\u91cc \u5f70<sup>1<\/sup>, \u9060\u85e4 \u667a\u53f2<sup>3,4<\/sup>, \u5bcc\u4e95 \u5065\u592a\u90ce<sup>2<\/sup>&nbsp; (<sup>1<\/sup>\u5c90\u961c\u85ac\u79d1\u5927\u5b66\u30fb\u751f\u5316\u5b66, <sup>2<\/sup>\u7523\u696d\u6280\u8853\u7dcf\u5408\u7814\u7a76\u6240 (AIST)\u30fb\u4eba\u5de5\u77e5\u80fd\u7814\u7a76\u30bb\u30f3\u30bf\u30fc, <sup>3<\/sup>\u5c90\u961c\u5927\u5b66\u5927\u5b66\u9662\u9023\u5408\u5275\u85ac\u533b\u7642\u60c5\u5831\u7814\u7a76\u79d1, <sup>4<\/sup>\u5c90\u961c\u5927\u5b66\u30fbCOMIT)<\/p>\n\n\n\n<p><strong>PO-025<br><\/strong>RNAMergeDistill: Multi-Teacher Knowledge Distillation for RNA Language Model<br>RNAMergeDistill: \u591a\u6559\u5e2b\u77e5\u8b58\u84b8\u7559\u306b\u3088\u308bRNA\u8a00\u8a9e\u30e2\u30c7\u30eb\u306e\u958b\u767a<br>\u6a4b\u672c \u548c\u78e8<sup>1<\/sup>, \u5c71\u7530 \u5553\u4ecb<sup>2<\/sup>, \u6d5c\u7530 \u9053\u662d<sup>1,3,4 <\/sup>&nbsp;(<sup>1<\/sup>\u65e9\u7a32\u7530\u5927\u5b66,<sup> 2<\/sup>\u30da\u30f3\u30b7\u30eb\u30d9\u30cb\u30a2\u5927\u5b66, <sup>3<\/sup>\u7523\u696d\u6280\u8853\u7dcf\u5408\u7814\u7a76\u6240, <sup>4<\/sup>\u65e5\u672c\u533b\u79d1\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-026<br><\/strong>Few-Shot ADMET Prediction with a Meta-Learning Framework<br>\u30e1\u30bf\u30e9\u30fc\u30cb\u30f3\u30b0\u3092\u7528\u3044\u305f\u5c11\u6570\u30c7\u30fc\u30bf\u74b0\u5883\u4e0b\u3067\u306eADMET\u4e88\u6e2c\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u306e\u69cb\u7bc9<br>\u9234\u5ca1 \u62d3\u4e5f<sup>1,2<\/sup>, \u6e05\u6c34 \u79c0\u5e78<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66\u5927\u5b66\u9662\u533b\u6b6f\u5b66\u7dcf\u5408\u7814\u7a76\u79d1, <sup>2<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66\u7dcf\u5408\u7814\u7a76\u9662M&amp;D\u30c7\u30fc\u30bf\u79d1\u5b66\u30bb\u30f3\u30bf\u30fcAI\u30b7\u30b9\u30c6\u30e0\u533b\u79d1\u5b66\u5206\u91ce)<\/p>\n\n\n\n<p><strong>PO-027<br><\/strong>RaptGFN: RNA Aptamer Sequence Design with GFlowNets<br>GFlowNets: GFlowNets\u3092\u7528\u3044\u305fRNA\u30a2\u30d7\u30bf\u30de\u30fc\u914d\u5217\u306e\u8a2d\u8a08<br>\u677e\u672c \u82f1\u502b<sup>1<\/sup>, \u6d5c\u7530 \u9053\u662d<sup>1,2,3<\/sup>&nbsp; (<sup>1<\/sup>\u65e9\u7a32\u7530\u5927\u5b66, <sup>2<\/sup>\u7523\u696d\u7dcf\u5408\u7814\u7a76\u6240, <sup>3<\/sup>\u65e5\u672c\u533b\u79d1\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-028<br><\/strong>An LLM-Based Method for Translating Natural Language into Knowledge Graph Queries<br>LLM \u306b\u3088\u308b\u81ea\u7136\u8a00\u8a9e\u304b\u3089\u306e\u77e5\u8b58\u30b0\u30e9\u30d5\u30af\u30a8\u30ea\u7ffb\u8a33<br>\u6c38\u7a4d \u8f1d<sup>1<\/sup>, \u5b88\u5c4b \u52c7\u6a39<sup>2<\/sup>, \u5ddd\u5cf6 \u79c0\u4e00<sup>2<\/sup>, \u7247\u5c71 \u4fca\u660e<sup>2<\/sup>, \u6e05\u6c34 \u4f73\u5948<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u65e9\u7a32\u7530\u5927\u5b66, <sup>2<\/sup>\u30e9\u30a4\u30d5\u30b5\u30a4\u30a8\u30f3\u30b9\u7d71\u5408\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30bb\u30f3\u30bf\u30fc)<\/p>\n\n\n\n<p><strong>PO-029<br><\/strong>Development progress of Gl-idea, a system that automatically extracts sugar chain information from sugar chain images<br>\u7cd6\u9396\u753b\u50cf\u304b\u3089\u7cd6\u9396\u60c5\u5831\u3092\u81ea\u52d5\u7684\u306b\u62bd\u51fa\u3059\u308b\u30b7\u30b9\u30c6\u30e0GL-idea\u306e\u958b\u767a\u9032\u6357<br>\u585a\u7530 \u4f38\u6a39<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u5275\u4fa1\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-030<br><\/strong>Overcoming data scarcity of computational pathology in preclinical drug discovery: a case study for skin pharmacological evaluation assay<br>\u5c11\u6570\u30c7\u30fc\u30bf\u306b\u3088\u308b\u975e\u81e8\u5e8a\u75c5\u7406\u753b\u50cf\u30e2\u30c7\u30eb\u306e\u5b66\u7fd2: \u76ae\u819a\u7d44\u7e54\u85ac\u7406\u8a55\u4fa1\u30a2\u30c3\u30bb\u30a4\u30c7\u30fc\u30bf\u306b\u304a\u3051\u308b\u30b1\u30fc\u30b9\u30b9\u30bf\u30c7\u30a3<br>\u5409\u958b \u6cf0\u88d5<sup>1<\/sup>, \u9f4a\u85e4 \u9686\u592a<sup>2<\/sup>, \u6c0f\u5bb6 \u8b19<sup>2<\/sup>, \u5c71\u4e2d \u9f8d<sup>2<\/sup>, \u4e09\u7531 \u6587\u5f66<sup>2<\/sup>, \u6960\u539f \u6d0b\u4e4b<sup>1<\/sup>, \u6c34\u91ce \u5fe0\u5feb<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662\u85ac\u5b66\u7cfb\u7814\u7a76\u79d1,<sup> 2<\/sup>\u7530\u8fba\u4e09\u83f1\u88fd\u85ac \u5275\u85ac\u672c\u90e8 \u5275\u85ac\u57fa\u76e4\u7814\u7a76\u6240 \u30d0\u30a4\u30aa\u30a4\u30f3\u30d5\u30a9\u30de\u30c6\u30a3\u30af\u30b9\u30b0\u30eb\u30fc\u30d7)<\/p>\n\n\n\n<p><strong>PO-031<br><\/strong>Development of a controllable model for the generation of novel target RNA sequences for RNA-binding proteins using Prefix-tuning.<br>Prefix-tuning\u3092\u7528\u3044\u305fRNA\u7d50\u5408\u30bf\u30f3\u30d1\u30af\u8cea\u306e\u65b0\u898f\u6a19\u7684RNA\u914d\u5217\u751f\u6210\u306e\u305f\u3081\u306e\u5236\u5fa1\u53ef\u80fd\u306a\u30e2\u30c7\u30eb\u306e\u958b\u767a<br>\u6a2a\u5c71 \u6e90\u592a\u6717<sup>1,2<\/sup>, \u89d2 \u4fca\u8f14<sup>3,4<\/sup>, \u5c0f\u91ce\u53e3 \u771f\u5e83<sup>1<\/sup>, \u6d5c\u7530 \u9053\u662d<sup>1,2 <\/sup>&nbsp;(<sup>1<\/sup>\u65e9\u7a32\u7530\u5927\u5b66, <sup>2<\/sup>\u7523\u7dcf\u7814\u7d30\u80de\u5206\u5b50\u5de5\u5b66\u7814\u7a76\u90e8\u9580,<sup> 3<\/sup>\u6771\u4eac\u5927\u5b66, <sup>4<\/sup>\u4eac\u90fd\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-032<br><\/strong>Large generative foundation mRNA language modeling for mRNA design<br>mRNA\u8a2d\u8a08\u306e\u305f\u3081\u306e\u5927\u898f\u6a21\u751f\u6210\u578b\u57fa\u76e4mRNA\u8a00\u8a9e\u30e2\u30c7\u30eb\u306e\u958b\u767a<br>\u908a \u904d<sup>3<\/sup>, Zhang Yiming<sup>1<\/sup>, \u6d45\u4e95 \u6f54<sup>1<\/sup>, \u9f4b\u85e4 \u88d5<sup>1,2,3<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66, <sup>2<\/sup>\u7523\u696d\u6280\u8853\u7dcf\u5408\u7814\u7a76\u6240, <sup>3<\/sup>\u5317\u91cc\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-033<br><\/strong>Development of AI-Driven Digital Twins for Motion Control Learning in Laboratory Automation<br>\u5b9f\u9a13\u30ed\u30dc\u30c3\u30c8\u306e\u52d5\u4f5c\u5b66\u7fd2\u306e\u305f\u3081\u306eAI\u99c6\u52d5\u30c7\u30b8\u30bf\u30eb\u30c4\u30a4\u30f3\u306e\u958b\u767a<br>\u738b \u82b8\u6f84<sup>1<\/sup>, Adrien Mialland<sup class=\"\">2<\/sup>,&nbsp;\u5149\u5c71 \u7d71\u6cf0<sup class=\"\">2<\/sup>,&nbsp;\u9f4b\u85e4 \u88d5<sup class=\"\">1,2,3<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662 \u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1,&nbsp;<sup class=\"\">2<\/sup>\u7523\u696d\u6280\u8853\u7dcf\u5408\u7814\u7a76\u6240 \u4eba\u5de5\u77e5\u80fd\u7814\u7a76\u30bb\u30f3\u30bf\u30fc,&nbsp;<sup class=\"\">3<\/sup>\u5317\u91cc\u5927\u5b66 \u672a\u6765\u79d1\u5b66\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-034<br><\/strong>Annotating and discovering CAZymes with deep learning<br>\u6df1\u5c64\u5b66\u7fd2\u306b\u3088\u308b\u7cd6\u8cea\u6d3b\u6027\u9175\u7d20\u306e\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3\u3068\u767a\u898b<br>\u5f35 \u745e\u8ed2<sup>1<\/sup>, \u7dd2\u65b9&nbsp; \u535a\u4e4b<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u4eac\u90fd\u5927\u5b66\u5316\u5b66\u7814\u7a76\u6240\u30d0\u30a4\u30aa\u30a4\u30f3\u30d5\u30a9\u30de\u30c6\u30a3\u30af\u30b9\u30bb\u30f3\u30bf\u30fc)<\/p>\n\n\n\n<p><strong>PO-035<br><\/strong>Deep Ensemble Learning for Predicting Treatment Outcomes in Heart Failure<br>\u6df1\u5c64\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u5b66\u7fd2\u306b\u3088\u308b\u5fc3\u4e0d\u5168\u306e\u6cbb\u7642\u4e88\u5f8c\u306e\u4e88\u6e2c<br>\u51fa\u7c60 \u8429\u4eba<sup>1<\/sup>, \u5019 \u8061\u5fd7<sup>2<\/sup>, \u6234 \u54f2\u7693<sup>2<\/sup>, \u85e4\u7530 \u5bdb\u5948<sup>2<\/sup>, \u5c3e\u4e0a \u5065\u5150<sup>3<\/sup>, \u91ce\u6751 \u5f81\u592a\u90ce<sup>2<\/sup>, \u5c0f\u5ba4 \u4e00\u6210<sup>4<\/sup>, \u6ff1\u91ce \u6843\u5b50<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66\u533b\u5b66\u90e8\u9644\u5c5e\u75c5\u9662, <sup>3<\/sup>\u5948\u826f\u770c\u7acb\u533b\u79d1\u5927\u5b66\u9644\u5c5e\u75c5\u9662, <sup>4<\/sup>\u56fd\u969b\u533b\u7642\u798f\u7949\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-036<br><\/strong>mRNA Sequence Generation Conditioned on Regulatory Features via Diffusion Model<br>\u62e1\u6563\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u5236\u5fa1\u7684\u7279\u5fb4\u306b\u57fa\u3065\u304fmRNA\u914d\u5217\u751f\u6210<br>DAI CHUANKAI<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-037<br><\/strong>Integrating high-speed super-resolution microscopy and machine learning for image-based epigenetic profiling in Rett Syndrome<br>\u30ec\u30c3\u30c9\u75c7\u5019\u7fa4\u306b\u304a\u3051\u308b\u753b\u50cf\u30d9\u30fc\u30b9\u306e\u30a8\u30d4\u30b8\u30a7\u30cd\u30c6\u30a3\u30af\u30b9\u30d7\u30ed\u30d5\u30a1\u30a4\u30ea\u30f3\u30b0\u306e\u305f\u3081\u306e\u9ad8\u901f\u8d85\u89e3\u50cf\u9855\u5fae\u93e1\u3068\u6a5f\u68b0\u5b66\u7fd2\u306e\u7d71\u5408<br>Ab Ghani Nur Syatila<sup>1<\/sup>, \u738b \u82b8\u6f84<sup>2,3<\/sup>, Dutta Munmee<sup>3<\/sup>, \u8db3\u9054 \u4fca\u543e<sup>4<\/sup>, \u52a0\u85e4 \u85ab<sup>3,5<\/sup>, \u6ce2\u5e73 \u660c\u4e00<sup>3<\/sup>, \u5149\u5c71 \u7d71\u6cf0<sup>3<\/sup>, \u9f4b\u85e4 \u88d5<sup>1,2,3<\/sup>&nbsp; (<sup>1<\/sup>\u5317\u91cc\u5927\u5b66, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66, <sup>3<\/sup>\u7523\u696d\u6280\u8853\u7dcf\u5408\u7814\u7a76\u6240, <sup>4<\/sup>\u56fd\u7acb\u7814\u7a76\u958b\u767a\u6cd5\u4eba \u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u7814\u7a76\u6240, <sup>5<\/sup>\u7b51\u6ce2\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-038<br><\/strong>Accurate Variant Calling via Platform-Specific Error Modeling using Deep Learning<br>\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306b\u3088\u308b\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0\u7279\u7570\u7684\u30a8\u30e9\u30fc\u306e\u30e2\u30c7\u30eb\u5316\u3092\u901a\u3058\u305f\u9ad8\u7cbe\u5ea6\u30d0\u30ea\u30a2\u30f3\u30c8\u691c\u51fa<br>\u694a \u4ec1\u667a<sup>1<\/sup>, \u85e4\u91ce \u5065<sup>1<\/sup>, \u7b20\u539f \u96c5\u5f18<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662 \u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1 \u30e1\u30c7\u30a3\u30ab\u30eb\u60c5\u5831\u751f\u547d\u5c02\u653b, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662 \u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1 \u751f\u547d\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u30bb\u30f3\u30bf\u30fc)<\/p>\n\n\n\n<p><strong>PO-039<br><\/strong>Segmentation of 3D Time-lapse Images of C. elegans Embryos Using Deformable U-Net<br>Deformable U-Net\u306b\u3088\u308b\u7dda\u866b\u80da3D\u30bf\u30a4\u30e0\u30e9\u30d7\u30b9\u753b\u50cf\u306e\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3<br>Jingyu LI<sup>1<\/sup>, \u9060\u91cc \u7531\u4f73\u5b50<sup>1<\/sup>&nbsp; (<sup>1<\/sup>ritsumeikan university)<\/p>\n\n\n\n<p><strong>PO-040<br><\/strong>Segmentation of C. elegans Microscopy Images Using a Patch-Position-Conditioned Deep Learning Model<br>\u30d1\u30c3\u30c1\u4f4d\u7f6e\u3092\u6761\u4ef6\u4ed8\u3051\u305f\u6df1\u5c64\u5b66\u7fd2\u30e2\u30c7\u30eb\u306b\u3088\u308b\u7dda\u866b\u753b\u50cf\u306e\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3<br>\u5409\u7530 \u60a0\u7d00<sup>1<\/sup>, \u9060\u91cc \u7531\u4f73\u5b50<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u7acb\u547d\u9928\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-041<br><\/strong>Automatic Extraction of Cell Perturbation Effects from Biomedical Abstracts Using LLMs<br>LLM\u3092\u7528\u3044\u305f\u751f\u7269\u533b\u5b66\u6587\u732e\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u304b\u3089\u306e\u7d30\u80de\u6442\u52d5\u5f71\u97ff\u306e\u81ea\u52d5\u62bd\u51fa<br>\u84ee\u898b \u6b63\u6210<sup>2<\/sup>, \u677e\u6fa4 \u4eae\u8f14<sup>2<\/sup>, \u4e95\u5c3b \u9065\u58eb<sup>2<\/sup>, \u5c3e\u5d0e \u907c<sup>1,2&nbsp; <\/sup>(<sup>1<\/sup>\u7406\u5316\u5b66\u7814\u7a76\u6240, <sup>2<\/sup>\u7b51\u6ce2\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-042<br><\/strong>Comparative evaluation of methods to map literature-described cell and tissue names to ontological terms<br>\u6587\u732e\u306b\u8a18\u8ff0\u3055\u308c\u305f\u7d30\u80de\u30fb\u7d44\u7e54\u540d\u306e\u30aa\u30f3\u30c8\u30ed\u30b8\u30fc\u30de\u30c3\u30d4\u30f3\u30b0\u624b\u6cd5\u306e\u6bd4\u8f03\u8a55\u4fa1<br>\u6728\u6751 \u7d17\u5b63<sup>1<\/sup>, \u4e95\u5c3b \u9065\u58eb<sup>1<\/sup>, \u5c3e\u5d0e \u907c<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u7b51\u6ce2\u5927\u5b66, <sup>2<\/sup>\u7406\u5316\u5b66\u7814\u7a76\u6240)<\/p>\n\n\n\n<p><strong>PO-043<br><\/strong>Development of an LLM-based Metadata Normalization Method and Its Application to GEO Metadata<br>LLM\u3092\u7528\u3044\u305f\u30e1\u30bf\u30c7\u30fc\u30bf\u6b63\u898f\u5316\u624b\u6cd5\u306e\u958b\u767a\u3068GEO\u30e1\u30bf\u30c7\u30fc\u30bf\u3078\u306e\u9069\u7528<br>\u5b89\u6fa4 \u96bc\u4eba<sup>1,2<\/sup>, \u6728\u4e0b \u8ce2\u543e<sup>1,2,3<\/sup>&nbsp; (<sup>1<\/sup>Graduate School of Information Sciences, Tohoku University, <sup>2<\/sup>Tohoku Medical Megabank Organization, Tohoku University, <sup>3<\/sup>Institute of Development, Aging and Cancer, Tohoku University)<\/p>\n\n\n\n<p><strong>PO-044<br><\/strong>Investigation of Cell Relationship Extraction Using Language AI toward Understanding Cellular Relationships<br>\u7d30\u80de\u9593\u95a2\u4fc2\u6027\u306e\u4fef\u77b0\u306b\u5411\u3051\u305f\u8a00\u8a9e\u30e2\u30c7\u30eb\u306b\u3088\u308b\u7d30\u80de\u9593\u95a2\u4fc2\u6027\u62bd\u51fa\u306e\u691c\u8a0e<br>\u5409\u5ddd \u82bd\u751f<sup>1<\/sup>, \u6c34\u91ce \u5fe0\u5feb<sup>1<\/sup>, \u5927\u6238 \u967d\u5e73<sup>1<\/sup>, \u85e4\u672c \u7d18\u7f8e<sup>1<\/sup>, \u6960\u539f \u6d0b\u4e4b<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662\u85ac\u5b66\u7cfb\u7814\u7a76\u79d1\u5206\u5b50\u85ac\u7269\u52d5\u614b\u5b66\u6559\u5ba4)<\/p>\n\n\n\n<p><strong>PO-045<br><\/strong>Subcellular Localization and Protein Function May Influence the Pathogenic Potential of Missense Variants<br>\u30bf\u30f3\u30d1\u30af\u8cea\u6a5f\u80fd\u3068\u7d30\u80de\u5185\u5c40\u5728\u304c\u30df\u30b9\u30bb\u30f3\u30b9\u5909\u7570\u306e\u75c5\u539f\u6027\u306b\u4e0e\u3048\u308b\u5f71\u97ff\u306e\u89e3\u6790<br>\u8fbb \u654f\u4e4b<sup>1<\/sup>, \u6728\u672c \u611b\u4f51\u7436<sup>1<\/sup>, \u89d2\u91ce \u967d\u83dc\u7f8e<sup>1<\/sup>, \u571f\u65b9 \u6566\u53f8<sup>2<\/sup>&nbsp; (<sup>1<\/sup>\u4e09\u7530\u56fd\u969b\u79d1\u5b66\u5b66\u5712\u9ad8\u7b49\u5b66\u6821, <sup>2<\/sup>\u6771\u4eac\u85ac\u79d1\u5927\u5b66 \u751f\u547d\u79d1\u5b66\u90e8)<\/p>\n\n\n\n<p><strong>PO-046<br><\/strong>Spatially Resolved Transcriptomics Databases for Human Cancers: Current Status and Challenges<br>\u30d2\u30c8\u304c\u3093\u306e\u7a7a\u9593\u30c8\u30e9\u30f3\u30b9\u30af\u30ea\u30d7\u30c8\u30fc\u30e0\u3092\u5bfe\u8c61\u3068\u3057\u305f\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u73fe\u72b6\u3068\u554f\u984c\u70b9<br>\u9577\u8c37\u5ddd \u68ee\u96c4<sup>1<\/sup>, \u5c3e\u5d0e \u907c<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u7b51\u6ce2\u5927\u5b66 \u533b\u5b66\u533b\u7642\u7cfb \u751f\u547d\u533b\u79d1\u5b66\u57df \u30d0\u30a4\u30aa\u30a4\u30f3\u30d5\u30a9\u30de\u30c6\u30a3\u30af\u30b9\u7814\u7a76\u5ba4, <sup>2<\/sup>\u7406\u5316\u5b66\u7814\u7a76\u6240 \u751f\u547d\u6a5f\u80fd\u79d1\u5b66\u7814\u7a76\u30bb\u30f3\u30bf\u30fc AI\u751f\u7269\u5b66\u7814\u7a76\u30c1\u30fc\u30e0)<\/p>\n\n\n\n<p><strong>PO-047<br><\/strong>PanelSearch: A Flexible and Customizable Gene Panel Platform for Rare Disease Diagnostics<br>PanelSearch: \u5e0c\u5c11\u75be\u60a3\u8a3a\u65ad\u306e\u305f\u3081\u306e\u67d4\u8edf\u304b\u3064\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u53ef\u80fd\u306a\u907a\u4f1d\u5b50\u30d1\u30cd\u30eb\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0<br>\u7533 \u5728\u7d0b<sup>1<\/sup>, \u5c71\u53e3 \u6566\u5b50<sup>2<\/sup>, \u5ddd\u5d8b \u5b9f\u82d7<sup>1<\/sup>, \u624d\u6d25 \u6d69\u667a<sup>3<\/sup>, \u85e4\u539f \u8c4a\u53f2<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u60c5\u5831\u30fb\u30b7\u30b9\u30c6\u30e0\u7814\u7a76\u6a5f\u69cb \u30e9\u30a4\u30d5\u30b5\u30a4\u30a8\u30f3\u30b9\u7d71\u5408\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30bb\u30f3\u30bf\u30fc, <sup>2<\/sup>\u6771\u4eac\u90fd\u5e02\u5927\u5b66 \u7dcf\u5408\u7406\u5de5\u5b66\u7814\u7a76\u79d1\u60c5\u5831\u5c02\u653b, <sup>3<\/sup>\u6d5c\u677e\u533b\u79d1\u5927\u5b66 \u533b\u5b66\u90e8 \u533b\u5316\u5b66\u8b1b\u5ea7)<\/p>\n\n\n\n<p><strong>PO-048<\/strong> \/ HT-603<strong><br><\/strong>ChIP-Atlas 3.0: a data-mining suite to explore chromosome architecture together with large-scale regulome data<br>ChIP-Atlas 3.0: \u67d3\u8272\u4f53\u69cb\u9020\u60c5\u5831\u3092\u7db2\u7f85\u3057\u305f\u30a8\u30d4\u30b2\u30ce\u30df\u30af\u30b9\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9<br>\u6c96 \u771f\u5f25<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u718a\u672c\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-049<br><\/strong>NanbyoData: A Cross-Domain Integration Platform for Rare Disease Knowledge Sharing<br>NanbyoData: \u5e0c\u5c11\u96e3\u75c5\u75be\u60a3\u306b\u304a\u3051\u308b\u77e5\u8b58\u5171\u6709\u306e\u305f\u3081\u306e\u5206\u91ce\u6a2a\u65ad\u578b\u7d71\u5408\u57fa\u76e4<br>\u7d30\u7530 \u6b63\u6075<sup>1<\/sup>, \u9ad8\u6708 \u7167\u6c5f<sup>1<\/sup>, \u7533 \u5728\u7d0b<sup>1<\/sup>, \u4e09\u6a4b \u4fe1\u5b5d<sup>1<\/sup>, \u85e4\u539f \u8c4a\u53f2<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u5927\u5b66\u5171\u540c\u5229\u7528\u6a5f\u95a2\u6cd5\u4eba \u60c5\u5831\u30fb\u30b7\u30b9\u30c6\u30e0\u7814\u7a76\u6a5f\u69cb&nbsp; \u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u5171\u540c\u5229\u7528\u57fa\u76e4\u65bd\u8a2d&nbsp; \u30e9\u30a4\u30d5\u30b5\u30a4\u30a8\u30f3\u30b9\u7d71\u5408\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30bb\u30f3\u30bf\u30fc)<\/p>\n\n\n\n<p><strong>PO-050<br><\/strong>Development of high-speed GPU database for whole genome sequencing data<br>\u5168\u30b2\u30ce\u30e0\u30c7\u30fc\u30bf\u691c\u7d22\u306e\u305f\u3081\u306e\u9ad8\u901fGPU\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u958b\u767a<br>\u6b63\u6728 \u7d00\u9686<sup>1<\/sup>, \u5409\u6ca2 \u512a\u82b1<sup>1<\/sup>, \u4f50\u4e45\u9593 \u670b\u5bdb<sup>1<\/sup>, \u5185\u7530 \u5229\u6587<sup>1<\/sup>, \u5c0f\u91ce \u7965\u6b63<sup>2<\/sup>, \u7247\u5c71 \u7434\u7d75<sup>2<\/sup>, \u4e95\u5143 \u6e05\u54c9<sup>2<\/sup>&nbsp; (<sup>1<\/sup>\u4e09\u4e95\u60c5\u5831\u682a\u5f0f\u4f1a\u793e, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66\u533b\u79d1\u5b66\u7814\u7a76\u6240\u30d2\u30c8\u30b2\u30ce\u30e0\u89e3\u6790\u30bb\u30f3\u30bf\u30fc)<\/p>\n\n\n\n<p><strong>PO-051<br><\/strong>ATTED-II: Plant Gene Coexpression Database with Improved Support for Species Comparison<br>ATTED-II: \u7a2e\u9593\u6bd4\u8f03\u3092\u5f37\u5316\u3057\u305f\u690d\u7269\u306e\u907a\u4f1d\u5b50\u5171\u767a\u73fe\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9<br>\u5927\u6797 \u6b66<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u5317\u5927\u5b66\u5927\u5b66\u9662\u60c5\u5831\u79d1\u5b66\u7814\u7a76\u79d1, <sup>2<\/sup>\u6771\u5317\u5927\u5b66\u5909\u52d5\u74b0\u5883\u30a8\u30b3\u30b7\u30b9\u30c6\u30e0\u9ad8\u7b49\u7814\u7a76\u6240)<\/p>\n\n\n\n<p><strong>PO-052<br><\/strong>Extension of C. elegans GlycoGene DataBase<br>C. elegans GlycoGene DataBase\u306e\u62e1\u5f35<br>\u5317\u91ce \u4fe1\u660e<sup>1<\/sup>, \u5869\u7530 \u6b63\u660e<sup>1<\/sup>, \u30c6\u30a4\u30e9\u30fc \u5e78\u6075<sup>1<\/sup>, \u674e \u6b63\u57fa<sup>1<\/sup>, \u6728\u4e0b \u8056\u5b50<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u5275\u4fa1\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-053<br><\/strong>Proposal of a batch effect correction method applicable to confounding scenarios<br>\u4ea4\u7d61\u30b7\u30ca\u30ea\u30aa\u306b\u5bfe\u51e6\u53ef\u80fd\u306a\u30d0\u30c3\u30c1\u52b9\u679c\u88dc\u6b63\u624b\u6cd5\u306e\u63d0\u6848<br>\u5ee3\u4e2d \u8b19\u4e00<sup>1<\/sup>, \u5b89\u7530 \u77e5\u5f18<sup>1<\/sup>, \u8c4a\u6751 \u5d07<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u65e5\u7acb\u88fd\u4f5c\u6240)<\/p>\n\n\n\n<p><strong>PO-054<br><\/strong>Graph neural network-based prediction of transcription factors inducing direct reprogramming<br>\u30b0\u30e9\u30d5\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u3088\u308b\u30c0\u30a4\u30ec\u30af\u30c8\u30ea\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u3092\u8a98\u5c0e\u3059\u308b\u8ee2\u5199\u56e0\u5b50\u306e\u4e88\u6e2c<br>\u5ddd\u5d0e \u77ad\u592a<sup>1<\/sup>, \u7af9\u672c \u548c\u5e83<sup>1<\/sup>, \u6ff1\u91ce \u6843\u5b50<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-055<br><\/strong>ES Cell-Derived Organoids for ASD Modeling and AI-Driven Phenotypic Characterization<br>ES\u7d30\u80de\u7531\u6765\u30aa\u30eb\u30ac\u30ce\u30a4\u30c9\u3092\u7528\u3044\u305fASD\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9\u3068AI\u306b\u3088\u308b\u8868\u73fe\u578b\u89e3\u6790<br>\u6a4b\u722a \u7f8e\u840c<sup>1<\/sup>, \u66fd\u6839 \u79c0\u5b50<sup>1<\/sup>, \u4e2d\u6fa4 \u9ebb\u8863<sup>2<\/sup>, \u7389\u7530 \u5609\u7d00<sup>2<\/sup>&nbsp; (<sup>1<\/sup>\u6a2a\u6d5c\u85ac\u79d1\u5927\u5b66, <sup>2<\/sup>\u5f18\u524d\u5927\u5b66\u5927\u5b66\u533b\u5b66\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-056<br><\/strong>Predicting Cell States Using Data-Driven Dynamical Systems<br>\u30c7\u30fc\u30bf\u99c6\u52d5\u578b\u2f12\u5b66\u7cfb\u306b\u3088\u308b\u7d30\u80de\u72b6\u614b\u4e88\u6e2c<br>\u4eba\u898b \u82b1\u5e06<sup>1<\/sup>, \u52a0\u85e4 \u6709\u5df1<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u5927\u962a\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-057<\/strong> \/ HT-201<strong><br><\/strong>Drug-induced cis-regulatory elements in human hepatocytes affect molecular phenotypes associated with adverse reactions<br>\u85ac\u7269\u526f\u4f5c\u7528\u306b\u95a2\u9023\u3059\u308b\u5206\u5b50\u8868\u73fe\u578b\u3092\u5236\u5fa1\u3059\u308b\u85ac\u5264\u5fdc\u7b54\u6027\u30b7\u30b9\u5236\u5fa1\u30a8\u30ec\u30e1\u30f3\u30c8<br>\u5ddd\u8def \u82f1\u54c9<sup>1<\/sup>, \u9f4a\u85e4 \u7d17\u5e0c<sup>1<\/sup>, \u548c\u7530 \u6dbc\u5b50<sup>1<\/sup>, \u897f\u6751 \u53cb\u679d<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>Tokyo Metropolitan Institute of Medical Science)<\/p>\n\n\n\n<p><strong>PO-058<br><\/strong>Comparison of fish skin mucus microbiota composition using RNA-seq and shotgun metagenomics<br>RNA-seq\u3068\u30b7\u30e7\u30c3\u30c8\u30ac\u30f3\u30e1\u30bf\u30b2\u30ce\u30e0\u3092\u7528\u3044\u305f\u9b5a\u76ae\u819a\u7c98\u6db2\u306e\u83cc\u53e2\u7d44\u6210\u6bd4\u8f03<br>\u4eca\u6751 \u5343\u7d75<sup>1<\/sup>, \u53e4\u7530 \u82b3\u4e00<sup>1<\/sup>, \u7530\u4e2d \u79c0\u5178<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>(\u682a)\u8c4a\u7530\u4e2d\u592e\u7814\u7a76\u6240)<\/p>\n\n\n\n<p><strong>PO-059<br><\/strong>Dynamic Network Biomarker-Based Detection of the Pre-disease state in an Atopic Dermatitis Mouse Model<br>\u52d5\u7684\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30d0\u30a4\u30aa\u30de\u30fc\u30ab\u30fc\u306b\u3088\u308b\u30a2\u30c8\u30d4\u30fc\u6027\u76ae\u819a\u708e\u30e2\u30c7\u30eb\u30de\u30a6\u30b9\u306b\u304a\u3051\u308b\u672a\u75c5\u691c\u51fa<br>\u6cc9\u6c34 \u53cb\u6d0b<sup>1<\/sup>, \u5ca9\ufa11 \u822a\u592a\u90ce<sup>1<\/sup>, \u5408\u539f \u4e00\u5e78<sup>2<\/sup>&nbsp; (<sup>1<\/sup>\u30de\u30eb\u30db\u682a\u5f0f\u4f1a\u793e, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66 \u30cb\u30e5\u30fc\u30ed\u30a4\u30f3\u30c6\u30ea\u30b8\u30a7\u30f3\u30b9\u56fd\u969b\u7814\u7a76\u6a5f\u69cb)<\/p>\n\n\n\n<p><strong>PO-060<br><\/strong>Benchmarking Robust PCA Methods for scRNA-seq Dimensionality Reduction<br>scRNA-seq\u306b\u304a\u3051\u308b\u6b21\u5143\u524a\u6e1b\u306e\u305f\u3081\u306e\u30ed\u30d0\u30b9\u30c8PCA\u624b\u6cd5\u306e\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u8a55\u4fa1<br>\u848b \u601d\u6f84<sup>2<\/sup>, \u6d5c\u7530 \u9053\u662d<sup>1,2,3<\/sup>&nbsp; (<sup>1<\/sup>\u7523\u696d\u6280\u8853\u7dcf\u5408\u7814\u7a76\u6240, <sup>2<\/sup>\u65e9\u7a32\u7530\u5927\u5b66, <sup>3<\/sup>\u65e5\u672c\u533b\u79d1\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-061<br><\/strong>Development of a machine learning method to predict treatment drugs based on functional relationships among biomolecules<br>\u751f\u4f53\u5206\u5b50\u306e\u6a5f\u80fd\u7684\u95a2\u4fc2\u6027\u304b\u3089\u75be\u60a3\u6cbb\u7642\u5019\u88dc\u85ac\u3092\u4e88\u6e2c\u3059\u308b\u6a5f\u68b0\u5b66\u7fd2\u624b\u6cd5\u306e\u958b\u767a<br>\u677e\u5143 \u60a0\u7fd4<sup>1<\/sup>, \u5409\u7530 \u5eb7\u4ecb<sup>1<\/sup>, \u6960\u672c \u670b\u4e00\u90ce<sup>1<\/sup>, \u5e73 \u9806\u4e00<sup>1<\/sup>, \u5c71\u897f \u82b3\u88d5<sup>2<\/sup>, \u5ca9\u7530 \u901a\u592b<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66, <sup>2<\/sup>\u540d\u53e4\u5c4b\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-062<br><\/strong>Omics-based discovery of candidate drugs for osteoarthritis<br>\u30aa\u30df\u30af\u30b9\u60c5\u5831\u306b\u57fa\u3065\u304f\u5909\u5f62\u6027\u95a2\u7bc0\u75c7\u6cbb\u7642\u5019\u88dc\u85ac\u306e\u63a2\u7d22<br>\u9f8d \u77e5\u5fb31, \u5ca9\u7530 \u901a\u592b<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-063<br><\/strong>Single-Cell Multi-Omics Analysis of Heterogeneity During Human iPS Cell Reprogramming<br>\u30d2\u30c8iPS\u7d30\u80de\u8a98\u5c0e\u306b\u304a\u3051\u308b\u518d\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u4e0d\u5747\u4e00\u6027\u306e\u5358\u4e00\u7d30\u80de\u30de\u30eb\u30c1\u30aa\u30df\u30af\u89e3\u6790<br>xu ruiqi<sup>1<\/sup>, \u6b63\u4e95 \u8061\u7f8e<sup>1<\/sup>, \u4e2d\u5ddd \u8aa0\u4eba<sup>1<\/sup>, \u6cb3\u53e3 \u7406\u7d17<sup>1<\/sup>&nbsp; (<sup>1<\/sup>CIRA, Kyoto University)<\/p>\n\n\n\n<p><strong>PO-064<br><\/strong>Comprehensive Analysis of mRNA Fate Determination Regulated by Transcription and Translation Initiation Sites Shifts in Plants<br>\u690d\u7269\u306e\u8ee2\u5199\u958b\u59cb\u70b9\u53ca\u3073\u7ffb\u8a33\u958b\u59cb\u70b9\u306e\u5909\u5316\u304c\u53ca\u307c\u3059mRNA\u306e\u904b\u547d\u6c7a\u5b9a\u306b\u95a2\u3059\u308b\u7db2\u7f85\u7684\u89e3\u6790<br>\u9053\u4e0b \u6ec9\u4eba<sup>1<\/sup>, \u6817\u5c71 \u670b\u5b50<sup>2<\/sup>, \u6cb3\u5185 \u6b63\u6cbb<sup>1,2<\/sup>, \u677e\u4e95 \u5357<sup>2<\/sup>, \u8494\u7530 \u7531\u5e03\u5b50<sup>1,2<\/sup>, \u6817\u539f \u5fd7\u592b<sup>2<\/sup>&nbsp; (<sup>1<\/sup>\u524d\u6a4b\u5de5\u79d1\u5927\u5b66, <sup>2<\/sup>\u7406\u7814CSRS)<\/p>\n\n\n\n<p><strong>PO-065<br><\/strong>Mechanism of Fibrotic Wall Formation at the Liver Tumor Boundary Using Spatial Autocorrelation Analysis<br>\u7a7a\u9593\u7684\u81ea\u5df1\u76f8\u95a2\u89e3\u6790\u3092\u7528\u3044\u305f\u304c\u3093\u5883\u754c\u90e8\u306b\u304a\u3051\u308b\u7dda\u7dad\u6027\u58c1\u5f62\u6210\u30e1\u30ab\u30cb\u30ba\u30e0\u306e\u89e3\u660e<br>\u91ce\u4e2d \u5141\u5e7e<sup>1<\/sup>, \u8d8a\u524d \u4f73\u5948\u6075<sup>1<\/sup>, \u795e\u8c37 \u77e5\u61b2<sup>1<\/sup>, \u6b66\u85e4 \u82b3\u7f8e<sup>1<\/sup>, \u85e4\u4e95 \u82f1\u6a39<sup>1<\/sup>, \u5b5d\u6a4b \u8ce2\u4e00<sup>1<\/sup>, \u9ad8\u6a4b \u907c<sup>2<\/sup>, \u5c0f\u7389 \u5c1a\u5b8f<sup>2<\/sup>, \u5927\u8c37 \u76f4\u5b50<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u5927\u962a\u516c\u7acb\u5927\u5b66, <sup>2<\/sup>\u5927\u962a\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-066<br><\/strong>Comprehensive Analysis of Characteristics of Multi-Nucleotide Variants (MNV) in the Japanese Population<br>\u65e5\u672c\u4eba\u96c6\u56e3\u306b\u304a\u3051\u308b\u591a\u5869\u57fa\u5909\u7570\uff08MNV\uff09\u306e\u6027\u72b6\u306e\u7db2\u7f85\u7684\u89e3\u6790<br>\u5b89\u7530 \u5343\u4e03<sup>1,2<\/sup>, \u5c3e\u5d0e \u907c<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u7b51\u6ce2\u5927\u5b66 \u533b\u5b66\u533b\u7642\u7cfb \u751f\u547d\u533b\u79d1\u5b66\u57df \u30d0\u30a4\u30aa\u30a4\u30f3\u30d5\u30a9\u30de\u30c6\u30a3\u30af\u30b9\u7814\u7a76\u5ba4, <sup>2<\/sup>\u7b51\u6ce2\u5927\u5b66\u751f\u547d\u74b0\u5883\u5b66\u7fa4\u751f\u7269\u5b66\u985e)<\/p>\n\n\n\n<p><strong>PO-067<br><\/strong>Integration of co-expression relationship across multiple datasets based on extended Pan- &amp; Core-network approaches<br>\u62e1\u5f35\u3055\u308c\u305f\u30d1\u30fc\u30f3\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u304a\u3088\u3073\u30b3\u30a2\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u624b\u6cd5\u306b\u57fa\u3065\u304f\u8907\u6570\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u304a\u3051\u308b\u5171\u767a\u73fe\u95a2\u4fc2\u306e\u7d71\u5408<br>Zhenbo Jiang<sup>1<\/sup>&nbsp; (<sup>1<\/sup>NIBB)<\/p>\n\n\n\n<p><strong>PO-068<br><\/strong>A cross-population compendium of gene\u2013environment interactions uncovers the dynamics of genetic architecture<br>\u907a\u4f1d\u5b50\u2013\u74b0\u5883\u4ea4\u4e92\u4f5c\u7528\u306f\u907a\u4f1d\u7684\u69cb\u9020\u306e\u52d5\u7684\u5909\u52d5\u3092\u89e3\u660e\u3059\u308b<br>\u96e3\u6ce2 \u771f\u4e00<sup>1<\/sup>, \u66fd\u6839\u539f \u7a76\u4eba<sup>1<\/sup>, \u30d0\u30a4\u30aa\u30d0\u30f3\u30af \u30b8\u30e3\u30d1\u30f3<sup>2<\/sup>, \u677e\u7530 \u6d69\u4e00<sup>3<\/sup>, \u5ca1\u7530 \u968f\u8c61<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662\u533b\u5b66\u7cfb\u7814\u7a76\u79d1\u907a\u4f1d\u60c5\u5831\u5b66, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66\u533b\u79d1\u5b66\u7814\u7a76\u6240,<sup> 3<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662 \u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1 \u30e1\u30c7\u30a3\u30ab\u30eb\u60c5\u5831\u751f\u547d\u5c02\u653b\u30af\u30ea\u30cb\u30ab\u30eb\u30b7\u30fc\u30af\u30a8\u30f3\u30b9\u5206\u91ce)<\/p>\n\n\n\n<p><strong>PO-069<br><\/strong>MitoCOMON: A whole mitochondrial DNA sequencing by universal primer design and long overlapping amplicons assembly<br>MitoCOMON\u6cd5\uff1a\u30e6\u30cb\u30d0\u30fc\u30b5\u30eb\u30d7\u30e9\u30a4\u30de\u30fc\u8a2d\u8a08\u3068\u91cd\u8907\u30a2\u30f3\u30d7\u30ea\u30b3\u30f3\u306e\u30a2\u30bb\u30f3\u30d6\u30ea\u306b\u3088\u308b\u5b8c\u5168\u30df\u30c8\u30b3\u30f3\u30c9\u30ea\u30a2\u914d\u5217\u89e3\u8aad<br>\u53e4\u7530 \u82b3\u4e00<sup>1<\/sup>, \u57a3\u7530 \u771f\u5948\u7f8e<sup>1<\/sup>, \u7530\u4e2d \u79c0\u5178<sup>1<\/sup>&nbsp; (<sup>1<\/sup>(\u682a)\u8c4a\u7530\u4e2d\u592e\u7814\u7a76\u6240)<\/p>\n\n\n\n<p><strong>PO-070<br><\/strong>Building a Cloud Platform for Fully Automated and Efficient Genome Analysis<br>\u30b2\u30ce\u30e0\u89e3\u6790\u306e\u5168\u81ea\u52d5\u5316\u3068\u52b9\u7387\u5316\u3092\u5b9f\u73fe\u3059\u308b\u30af\u30e9\u30a6\u30c9\u74b0\u5883\u69cb\u7bc9<br>\u6728\u4e0b \u5927\u8f14<sup>1,2<\/sup>, \u4e45\u6211 \u6709\u7950\u7f8e<sup>1,2<\/sup>, \u95a2\u5bb6 \u53cb\u5b50<sup>1,2<\/sup>, \u6e6f\u539f \u609f\u5fd7<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u5408\u540c\u4f1a\u793eH.U.\u30b0\u30eb\u30fc\u30d7\u4e2d\u592e\u7814\u7a76\u6240, <sup>2<\/sup>\u682a\u5f0f\u4f1a\u793e\u30a8\u30b9\u30a2\u30fc\u30eb\u30a8\u30eb)<\/p>\n\n\n\n<p><strong>PO-071<br><\/strong>Splicing Junction-based Classifier for the Detection of Abnormal KEAP1-NRF2 System Activation<br>\u7570\u5e38\u306aKEAP1-NRF2\u30b7\u30b9\u30c6\u30e0\u6d3b\u6027\u5316\u3092\u691c\u51fa\u3059\u308b\u30b9\u30d7\u30e9\u30a4\u30b7\u30f3\u30b0\u30b8\u30e3\u30f3\u30af\u30b7\u30e7\u30f3\u306b\u57fa\u3065\u304f\u5206\u985e\u5668<br>Mateos Raul<sup>2<\/sup>, Winardi Wira <sup>1<\/sup>, Chiba Kenichi<sup>2<\/sup>, Okada Ai<sup>2<\/sup>, Suzuki Ayako<sup>3<\/sup>, Mitsuishi Yoichiro<sup>1<\/sup>, Shiraishi Yuichi<sup>2<\/sup>&nbsp; (<sup>1<\/sup>Juntendo University Graduate School of Medicine, <sup>2<\/sup>National Cancer Center Research Institute Japan, <sup>3<\/sup>Graduate School of Frontier Sciences, The University of Tokyo)<\/p>\n\n\n\n<p><strong>PO-072<br><\/strong>Evolutionary Conservation and Genomic Context of MER130 Elements in Tetrapod Species<br>Nguyen Hung-Ngoc<sup>1<\/sup>&nbsp; (<sup>1<\/sup>CBMS, Graduate School of Frontier Sciences, University of Tokyo)<\/p>\n\n\n\n<p><strong>PO-073<br><\/strong>Naviphy: a compass for the accurate phylogenetic tree inference<br>Naviphy: \u6b63\u78ba\u306a\u7cfb\u7d71\u6a39\u63a8\u5b9a\u306b\u5411\u3051\u305f\u7f85\u91dd\u76e4<br>\u938c\u7530 \u822a\u6bc5<sup>2<\/sup>, \u677e\u4e95 \u6c42<sup>1&nbsp; <\/sup>(<sup>1<\/sup>\u4eac\u90fd\u5927\u5b66\u30fb\u5316\u5b66\u7814\u7a76\u6240, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66\u30fb\u7406\u5b66\u7cfb\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-074<br><\/strong>ecNGS methods for detecting low-frequency somatic mutations and example of practical application<br>\u4f4e\u983b\u5ea6\u4f53\u7d30\u80de\u5909\u7570\u3092\u691c\u51fa\u3059\u308becNGS\u6280\u8853\u3068\u305d\u306e\u5b9f\u5fdc\u7528\u4f8b<br>\u4f0a\u6fa4 \u548c\u8f1d<sup>1<\/sup>, \u8af8\u89d2 \u6dbc\u4ecb<sup>1<\/sup>, \u6d25\u7530 \u96c5\u8cb4<sup>1<\/sup>, \u6749\u5c71 \u572d\u4e00<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u56fd\u7acb\u533b\u85ac\u54c1\u98df\u54c1\u885b\u751f\u7814\u7a76\u6240\u3000\u30b2\u30ce\u30e0\u5b89\u5168\u79d1\u5b66\u90e8)<\/p>\n\n\n\n<p><strong>PO-075<br><\/strong>Interval hashing: a simple algorithm for precise overlap detection in alpha satellite HOR regions<br>\u30a4\u30f3\u30bf\u30fc\u30d0\u30eb\u30cf\u30c3\u30b7\u30f3\u30b0: alpha satellite HOR\u9818\u57df\u306b\u304a\u3044\u3066\u6b63\u78ba\u306b\u30aa\u30fc\u30d0\u30fc\u30e9\u30c3\u30d7\u3092\u691c\u51fa\u3059\u308b\u305f\u3081\u306e\u7c21\u6f54\u306a\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0<br>\u9234\u6728 \u5275<sup>1<\/sup>, \u9808\u5ddd \u6b63\u5553<sup>1<\/sup>, \u5742\u672c \u7965\u99ff<sup>1<\/sup>, \u767d\u77f3 \u53cb\u4e00<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u7814\u7a76\u6240)<\/p>\n\n\n\n<p><strong>PO-076<br><\/strong>An Integrated, Reproducible, and Scalable Workflow for Ancient DNA Analysis<br>\u53e4DNA\u89e3\u6790\u306e\u9ad8\u52b9\u7387\u5316\u30fb\u9ad8\u7cbe\u5ea6\u5316\u306b\u5411\u3051\u305f\u7d71\u5408\u30ef\u30fc\u30af\u30d5\u30ed\u30fc<br>\u77f3\u8c37 \u5b54\u53f8<sup>1,2 <\/sup>&nbsp;(<sup>1<\/sup>\u91d1\u6ca2\u5927\u5b66 \u30b5\u30d4\u30a8\u30f3\u30b9\u9032\u5316\u533b\u5b66\u7814\u7a76\u30bb\u30f3\u30bf\u30fc, <sup>2<\/sup>\u91d1\u6ca2\u5927\u5b66 \u53e4\u4ee3\u6587\u660e\u30fb\u6587\u5316\u8cc7\u6e90\u5b66\u7814\u7a76\u6240)<\/p>\n\n\n\n<p><strong>PO-077<br><\/strong>Identification of important genes contributing to the virulence of Escherichia coli ST131<br>\u5927\u8178\u83ccST131\u306e\u75c5\u539f\u6027\u306b\u95a2\u4e0e\u3059\u308b\u4e3b\u8981\u907a\u4f1d\u5b50\u306e\u540c\u5b9a<br>\u9b8e\u5ddd \u667a\u7d00<sup>1<\/sup>, \u9234\u6728 \u5321\u5f18<sup>1<\/sup>, \u571f\u4e95 \u6d0b\u5e73<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u85e4\u7530\u533b\u79d1\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-078<br><\/strong>Enhancing chemical feature space by representation learning of microbe-compound networks<br>\u5fae\u751f\u7269\u2015\u5316\u5408\u7269\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u8868\u73fe\u5b66\u7fd2\u306b\u3088\u308b\u5316\u5408\u7269\u7279\u5fb4\u7a7a\u9593\u306e\u69cb\u7bc9<br>\u738b \u8d85<sup>1<\/sup>, \u9f4b\u85e4 \u88d5<sup>1,2,3<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662\u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1 ,<sup> 2<\/sup>\u5317\u91cc\u5927\u5b66\u672a\u6765\u5de5\u5b66\u90e8 , <sup>3<\/sup>\u7523\u696d\u6280\u8853\u7dcf\u5408\u7814\u7a76\u6240 \u4eba\u5de5\u77e5\u80fd\u7814\u7a76\u30bb\u30f3\u30bf\u30fc)<\/p>\n\n\n\n<p><strong>PO-079<br><\/strong>Classification of atrial fibrillation patients from pathophysiological images by feature extraction using deep learning and clustering<br>\u81ea\u5df1\u6559\u5e2b\u3042\u308a\u5b66\u7fd2\u3092\u7528\u3044\u305f\u5fc3\u623f\u7d44\u7e54\u50cf\u306e\u7279\u5fb4\u62bd\u51fa\u3068\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306b\u3088\u308b\u5fc3\u623f\u7d30\u52d5\u60a3\u8005\u306e\u5c64\u5225\u5316<br>\u77f3\u7aaa \u9234\u82d1<sup>1<\/sup>, \u9ad8\u6a4b \u4f51\u5f25<sup>2,3<\/sup>, \u5c71\u53e3 \u5c0a\u5247<sup>2<\/sup>, \u6ff1\u91ce \u6843\u5b50<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66, <sup>2<\/sup>\u4f50\u8cc0\u2f24\u5b66\u533b\u5b66\u90e8\u9644\u5c5e\u75c5\u9662,<sup> 3<\/sup>\u6771\u4eac\u5927\u5b66\u533b\u5b66\u90e8\u9644\u5c5e\u75c5\u9662)<\/p>\n\n\n\n<p><strong>PO-080<br><\/strong>Deep Learning-Based Prediction of Genetic Mutations using Myocardial Nucleus Staining Images in Patients with Heart Failure<br>\u5fc3\u7b4b\u7d30\u80de\u6838\u67d3\u8272\u753b\u50cf\u30c7\u30fc\u30bf\u304b\u3089\u5fc3\u4e0d\u5168\u306e\u539f\u56e0\u907a\u4f1d\u5b50\u5909\u7570\u3092\u4e88\u6e2c\u3059\u308b\u6df1\u5c64\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9<br>\u91ce\u6797 \u5178\u6717<sup>1<\/sup>, \u6234 \u54f2\u7693<sup>2<\/sup>, \u5019 \u8061\u5fd7<sup>2<\/sup>, \u85e4\u7530 \u5bdb\u5948<sup>2<\/sup>, \u91ce\u6751 \u5f81\u592a\u90ce<sup>2<\/sup>, \u5c0f\u5ba4 \u4e00\u6210<sup>3<\/sup>, \u6ff1\u91ce \u6843\u5b50<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66\u533b\u5b66\u90e8\u9644\u5c5e\u75c5\u9662, <sup>3<\/sup>\u56fd\u969b\u533b\u7642\u798f\u7949\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-081<br><\/strong>Exploring biomarkers for idiopathic pulmonary fibrosis using correlation analysis of serum extracellular vesicles proteomic and clinical datasets<br>\u8840\u6e05\u7d30\u80de\u5916\u5c0f\u80de\u30d7\u30ed\u30c6\u30aa\u30df\u30af\u30b9\u30c7\u30fc\u30bf\u3068\u81e8\u5e8a\u30c7\u30fc\u30bf\u306e\u76f8\u95a2\u89e3\u6790\u306b\u3088\u308b\u7279\u767a\u6027\u80ba\u7dda\u7dad\u75c7\u306e\u30d0\u30a4\u30aa\u30de\u30fc\u30ab\u30fc\u63a2\u7d22<br>\u4e0a\u689d \u967d\u5e73<sup>1,2,3<\/sup>, \u677e\u6751 \u53cb\u7f8e\u5b50<sup>2<\/sup>, \u98ef\u7530 \u7dd1<sup>2<\/sup>, \u5ca9\u7530 \u901a\u592b<sup>2<\/sup>, \u6fe1\u6728 \u771f\u4e00<sup>4<\/sup>, \u77f3\u5d0e \u654f\u7406<sup>4<\/sup>, \u52a0\u85e4 \u660e\u826f<sup>4<\/sup>, \u4f0a\u85e4 \u771e\u91cc<sup>6<\/sup>, \u6b66\u7530 \u5409\u4eba<sup>5<\/sup>, \u9ed2\u7530 \u6b63\u5b5d<sup>6<\/sup>, \u8db3\u7acb \u6df3<sup>6,7<\/sup>, \u590f\u76ee \u3084\u3088\u3044<sup>6,8<\/sup>, \u6c34\u53e3 \u8ce2\u53f8<sup>5,6<\/sup>, \u718a\u30ce\u90f7 \u6df3<sup>5<\/sup>, \u5c71\u897f \u82b3\u88d5<sup>3<\/sup> (<sup>1<\/sup>\u611b\u77e5\u770c\u304c\u3093\u30bb\u30f3\u30bf\u30fc\u7814\u7a76\u6240, <sup>2<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66, <sup>3<\/sup>\u540d\u53e4\u5c4b\u5927\u5b66, <sup>4<\/sup>\u5927\u5206\u5927\u5b66,<sup> 5<\/sup>\u5927\u962a\u5927\u5b66, <sup>6<\/sup>\u533b\u85ac\u57fa\u76e4\u30fb\u5065\u5eb7\u30fb\u6804\u990a\u7814\u7a76\u6240, <sup>7<\/sup>\u4eac\u90fd\u5927\u5b66, <sup>8<\/sup>\u5fb3\u5cf6\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-082<br><\/strong>Clinical big data analysis and deep learning model construction for disease prevention<br>\u75be\u60a3\u4e88\u9632\u306e\u305f\u3081\u306e\u81e8\u5e8a\u30d3\u30c3\u30b0\u30c7\u30fc\u30bf\u89e3\u6790\u3068\u6df1\u5c64\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9, \u5c71\u672c \u3072\u3088\u308a<sup>1<\/sup>, \u4e00\u30ce\u702c \u97f3\u8449<sup>2<\/sup>, \u68ee\u5c71 \u88d5\u96c5<sup>1<\/sup>, \u5c71\u897f \u82b3\u88d5<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u540d\u53e4\u5c4b\u5927\u5b66, <sup>2<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-083<br><\/strong>Prediction of therapeutic target molecules using TWAS and disease pathway information<br>TWAS\u3068\u75be\u60a3\u30d1\u30b9\u30a6\u30a7\u30a4\u60c5\u5831\u3092\u6d3b\u7528\u3057\u305f\u6cbb\u7642\u6a19\u7684\u5206\u5b50\u306e\u4e88\u6e2c<br>\u90f7 \u9065\u9999<sup>1<\/sup>, \u96e3\u6ce2 \u91cc\u5b50<sup>1<\/sup>, \u5c71\u897f \u82b3\u88d5<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u540d\u53e4\u5c4b\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-084<br><\/strong>Drug\u2013disease association analysis with medical data and its application to liver diseases<br>\u533b\u7642\u30c7\u30fc\u30bf\u306b\u304a\u3051\u308b\u85ac\u3068\u75be\u60a3\u306e\u9023\u95a2\u89e3\u6790\u3068\u809d\u75be\u60a3\u3078\u306e\u5fdc\u7528<br>\u67f4\u7530 \u84bc\u53f8<sup>1<\/sup>, \u82e5\u6797 \u4fca\u4e00<sup>3<\/sup>, \u5ca9\u7530 \u901a\u592b<sup>2<\/sup>, \u5c71\u897f \u82b3\u88d5<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u540d\u53e4\u5c4b\u5927\u5b66, <sup>2<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66, <sup>3<\/sup>\u4fe1\u5dde\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-085<\/strong> \/ HT-301<strong><br><\/strong>TRESOR: comprehensive discovery of therapeutic targets for orphan diseases via integration of GWAS and TWAS<br>TRESOR: GWAS\u3068TWAS\u306e\u878d\u5408\u306b\u3088\u308b\u5e0c\u5c11\u75be\u60a3\u306b\u5bfe\u3059\u308b\u5275\u85ac\u6a19\u7684\u5206\u5b50\u306e\u7db2\u7f85\u7684\u63a2\u7d22<br>\u96e3\u6ce2 \u91cc\u5b50<sup>1<\/sup>, \u5ca9\u7530 \u901a\u592b<sup>2<\/sup>, \u6fe1\u6728 \u771f\u4e00<sup>3<\/sup>, \u5927\u8c37 \u5247\u5b50<sup>1<\/sup>, \u5c71\u897f \u82b3\u88d5<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u540d\u53e4\u5c4b\u5927\u5b66,<sup> 2<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66,<sup> 3<\/sup>\u5927\u5206\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-086<br><\/strong>Survival Prediction of Cancer Patients Using Machine Learning Models<br>\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u304c\u3093\u60a3\u8005\u306e\u5bff\u547d\u4e88\u6e2c<br>\u4e0b\u6751 \u529f\u771f<sup>1<\/sup>, \u658e\u85e4 \u4ec1\u5fd7<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u7406\u79d1\u5927\u5b66, <sup>2<\/sup>\u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc)<\/p>\n\n\n\n<p><strong>PO-088<br><\/strong>Improved variant effect prediction by translocation of fitness landscape across homologs<br>\u9069\u5fdc\u5ea6\u5730\u5f62\u306e\u30db\u30e2\u30ed\u30b0\u9593\u306e\u79fb\u690d\u306b\u3088\u308b\u5909\u7570\u52b9\u679c\u4e88\u6e2c\u306e\u7cbe\u5ea6\u5411\u4e0a<br>\u8463 \u96f2\u98db<sup>1<\/sup>, Mialland Adrien<sup>3<\/sup>, \u9f4b\u85e4 \u88d5<sup>1,2,3<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662 \u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1, <sup>2<\/sup>\u5317\u91cc\u5927\u5b66 \u672a\u6765\u5de5\u5b66\u90e8, <sup>3<\/sup>\u7523\u696d\u6280\u8853\u7dcf\u5408\u7814\u7a76\u6240 \u4eba\u5de5\u77e5\u80fd\u7814\u7a76\u30bb\u30f3\u30bf\u30fc)<\/p>\n\n\n\n<p><strong>PO-089<br><\/strong>Mechanistic analysis of compounds that induce anti-aging reprogramming<br>\u6297\u8001\u5316\u30ea\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u3092\u8a98\u5c0e\u3059\u308b\u5316\u5408\u7269\u306e\u4f5c\u7528\u6a5f\u5e8f\u89e3\u6790<br>\u962a\u53e3 \u53cc\u8449<sup>1<\/sup>, \u7530\u4e2d \u672a\u6765<sup>1<\/sup>, \u96e3\u6ce2 \u91cc\u5b50<sup>1<\/sup>, \u4e0a\u689d \u967d\u5e73<sup>1<\/sup>,&nbsp; \u83ca\u6c60 \u7d00\u5e83<sup>2<\/sup>, \u5c71\u897f \u82b3\u88d5<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u540d\u53e4\u5c4b\u5927\u5b66, <sup>2<\/sup>\u682a\u5f0f\u4f1a\u793eFRACORA)<\/p>\n\n\n\n<p><strong>PO-090<br><\/strong>An Integrated Deep Learning Approach for the Differential Diagnosis of FUO using Clinical and PET\/CT Imaging Data<br>\u81e8\u5e8a\u304a\u3088\u3073PET\/CT\u753b\u50cf\u30c7\u30fc\u30bf\u3092\u7528\u3044\u305f\u539f\u56e0\u4e0d\u660e\u71b1\u306e\u9451\u5225\u8a3a\u65ad\u306b\u5bfe\u3059\u308b\u7d71\u5408\u7684\u6df1\u5c64\u5b66\u7fd2\u30a2\u30d7\u30ed\u30fc\u30c1<br>\u9673 \u6c11\u745e<sup>1<\/sup>, Zhou Yi<sup>2<\/sup>, Jiang Huidong<sup>3,4<\/sup>, Zhu Yuhan<sup>5<\/sup>, Zou Guanjie<sup>5<\/sup>, Chen Minqi<sup>2<\/sup>, Tian Rong<sup>2<\/sup>, Saigo Hiroto<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u4e5d\u5dde\u5927\u5b66, <sup>2<\/sup>\u56db\u5ddd\u5927\u5b66\u83ef\u897f\u75c5\u9662, <sup>3<\/sup>\u7406\u5316\u5b66\u7814\u7a76\u6240 \u9769\u65b0\u77e5\u80fd\u7d71\u5408\u7814\u7a76\u30bb\u30f3\u30bf\u30fc, <sup>4<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66, <sup>5<\/sup>\u6771\u4eac\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-091<br><\/strong>Analysis of Adverse Event Risk Using MIMIC-IV and Japanese Drug Package Inserts: A Comparison of Known and Unknown Adverse Events<br>MIMIC-\u2163\u3068\u533b\u85ac\u54c1\u6dfb\u4ed8\u6587\u66f8\u3092\u7528\u3044\u305f\u526f\u4f5c\u7528\u767a\u73fe\u30ea\u30b9\u30af\u306e\u89e3\u6790\u3068\u65e2\u77e5\u30fb\u672a\u77e5\u526f\u4f5c\u7528\u306e\u6bd4\u8f03\u691c\u8a0e<br>\u4e45\u4fdd\u7530 \u91c7\u4f73<sup>1<\/sup>, \u5ca1\u91ce \u60a0\u592a\u90ce<sup>1<\/sup>, \u5c71\u7530 \u548c\u7bc4<sup>1<\/sup>, \u6a0b\u5730 \u6b63\u6d69<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u5317\u5927\u5b66\u5927\u5b66\u9662\u60c5\u5831\u79d1\u5b66\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-092<br><\/strong>Construction of a Multimodal Knowledge Graph for Drug Repositioning<br>\u30c9\u30e9\u30c3\u30b0\u30ea\u30dd\u30b8\u30b7\u30e7\u30cb\u30f3\u30b0\u3092\u76ee\u6307\u3057\u305f\u30de\u30eb\u30c1\u30e2\u30fc\u30c0\u30eb\u30fb\u77e5\u8b58\u30b0\u30e9\u30d5\u306e\u69cb\u7bc9, \u9234\u6728 \u5d07\u82f1<sup>1,2<\/sup>, \u767d\u5ddd \u4e45\u5fd7<sup>1<\/sup>, \u4e95\u4e0a \u98db\u9ce5<sup>1<\/sup>, \u6e05\u6c34 \u79c0\u5e78<sup>2<\/sup>&nbsp; (<sup>1<\/sup>\u4eac\u90fd\u5927\u5b66, <sup>2<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-093<br><\/strong>Computational prediction of rejuvenation-inducing transcription factors in senescent cells<br>\u8001\u5316\u7d30\u80de\u3092\u82e5\u5316\u7d30\u80de\u3078\u5909\u63db\u3059\u308b\u8ee2\u5199\u56e0\u5b50\u5019\u88dc\u306e\u4e88\u6e2c<br>\u7530\u4e2d \u672a\u6765<sup>1<\/sup>, \u96e3\u6ce2 \u91cc\u5b50<sup>1<\/sup>, \u5c71\u897f \u82b3\u88d5<sup>1<\/sup>&nbsp; (<sup>1<\/sup> \u540d\u53e4\u5c4b\u5927\u5b66\u5927\u5b66\u9662 \u60c5\u5831\u5b66\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-094<br><\/strong>Genome based prediction model for mental disorders<br>\u907a\u4f1d\u60c5\u5831\u306b\u57fa\u3065\u304f\u7cbe\u795e\u75be\u60a3\u306e\u767a\u75c7\u4e88\u6e2c\u30e2\u30c7\u30eb\u69cb\u7bc9<br>\u4e2d\u7530 \u7950\u767b<sup>1<\/sup>, \u7267\u91ce \u80fd\u58eb<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u6771\u5317\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-095<br><\/strong>A Machine Learning Model for Predicting the Onset of ICI-induced Myocarditis<br>ICI\u8a98\u767a\u6027\u5fc3\u7b4b\u708e\u767a\u75c7\u4e88\u6e2c\u306e\u305f\u3081\u306e\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb<br>\u5927\u68ee \u60a0\u751f<sup>1<\/sup>, \u5c71\u5143 \u9ece\u5948<sup>4<\/sup>, \u6ff1\u91ce \u88d5\u7ae0<sup>1,2<\/sup>, \u4e2d\u8fbc \u6602\u5e0c<sup>1<\/sup>, \u5185\u5c71 \u5145\u4f51<sup>1<\/sup>, \u4e0a\u539f \u5b5d<sup>4<\/sup>, \u5ea7\u9593\u5473 \u7fa9\u4eba<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u5ca1\u5c71\u5927\u5b66\u3000\u81e8\u5e8a\u85ac\u5264\u5b66, <sup>2<\/sup>\u5ca1\u5c71\u5927\u5b66\u75c5\u9662\u3000\u85ac\u5264\u90e8, <sup>3<\/sup>\u5ca1\u5c71\u5927\u5b66\u5927\u5b66\u9662\u3000\u533b\u6b6f\u85ac\u5b66\u7dcf\u5408\u7814\u7a76\u79d1\u3000\u85ac\u52b9\u89e3\u6790\u5b66\u6559\u5ba4, <sup>4<\/sup>\u5ca1\u5c71\u5927\u5b66\u3000\u5b66\u8853\u7814\u7a76\u9662\u533b\u6b6f\u85ac\u5b66\u57df\u3000\u85ac\u52b9\u89e3\u6790\u5b66)<\/p>\n\n\n\n<p><strong>PO-096<br><\/strong>Prediction of metastasis based on gene expression in normal tissue adjacent to tumor<br>\u304c\u3093\u96a3\u63a5\u6b63\u5e38\u7d44\u7e54\uff08NAT\uff09\u306e\u907a\u4f1d\u5b50\u767a\u73fe\u60c5\u5831\u306b\u57fa\u3065\u3044\u305f\u304c\u3093\u9060\u9694\u8ee2\u79fb\u306e\u4e88\u6e2c\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9<br>\u5c0f\u91ce \u771f\u4e00\u6717<sup>1<\/sup>, \u7267\u91ce \u80fd\u58eb<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u5317\u5927\u5b66\u3000\u751f\u547d\u79d1\u5b66\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-097<br><\/strong>Exploration and Validation of Prophylactic Drugs for Immune Checkpoint Inhibitor-Induced Myocarditis<br>\u514d\u75ab\u30c1\u30a7\u30c3\u30af\u30dd\u30a4\u30f3\u30c8\u963b\u5bb3\u85ac\u8a98\u767a\u5fc3\u7b4b\u708e\u306e\u4e88\u9632\u85ac\u63a2\u7d22\u30fb\u691c\u8a3c<br>\u5c71\u5143 \u9ece\u5948<sup>1<\/sup>, \u6ff1\u91ce \u88d5\u7ae0<sup>2,3<\/sup>, \u8d64\u7530 \u8ce2\u5fc3<sup>3<\/sup>, \u68ee\u5ddd \u529b\u6597<sup>3<\/sup>, \u5ea7\u9593\u5473 \u7fa9\u4eba<sup>2,3<\/sup>, \u4e0a\u539f \u5b5d<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u5ca1\u5c71\u5927\u5b66\u3000\u5b66\u8853\u7814\u7a76\u9662\u533b\u6b6f\u85ac\u5b66\u57df\u3000\u85ac\u52b9\u89e3\u6790\u5b66, <sup>2<\/sup>\u5ca1\u5c71\u5927\u5b66\u75c5\u9662\u3000\u85ac\u5264\u90e8, <sup>3<\/sup>\u5ca1\u5c71\u5927\u5b66\u3000\u81e8\u5e8a\u85ac\u5264\u5b66)<\/p>\n\n\n\n<p><strong>PO-098<br><\/strong>A filtering technique of full-length meta 16S rRNA analysis of intra-tissue microbiome analysis in colorectal cancer<br>\u5927\u8178\u764c\u306e\u7d44\u7e54\u5185\u7d30\u83cc\u53e2\u89e3\u6790\u3092\u884c\u3046\u305f\u3081\u306efiltering\u624b\u6cd5<br>\u7027\u539f \u901f\u4ec1<sup>1<\/sup>, \u7530\u5cf6 \u967d\u4ecb<sup>1<\/sup>, \u82e5\u4e95 \u4fca\u6587<sup>1<\/sup>, \u5965\u7530 \u4fee\u4e8c\u90ce<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u65b0\u6f5f\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-099<br><\/strong>Meta-Analysis of Host Transcriptomic Signatures to Assess Infection and Pathogenicity of Animal-Derived Viruses<br>\u5bbf\u4e3b\u306e\u767a\u73fe\u5fdc\u7b54\u3092\u624b\u304c\u304b\u308a\u3068\u3057\u305f\u52d5\u7269\u7531\u6765\u30a6\u30a4\u30eb\u30b9\u306e\u691c\u51fa\u3068\u6027\u8cea\u4e88\u6e2c<br>\u5ddd\u5d0e \u7d14\u83dc<sup>1<\/sup>, \u4f0a\u6771 \u6f64\u5e73<sup>4<\/sup>, \u6d5c\u7530 \u9053\u662d<sup>3<\/sup>, \u9234\u6728 \u5fe0\u6a39<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u5343\u8449\u5927\u5b66 \u533b\u5b66\u7814\u7a76\u9662, <sup>2<\/sup>\u56fd\u7acb\u611f\u67d3\u75c7\u7814\u7a76\u6240 \u611f\u67d3\u75c5\u7406\u90e8, <sup>3<\/sup>\u65e9\u7a32\u7530\u5927\u5b66 \u7406\u5de5\u5b66\u8853\u9662, <sup>4<\/sup>\u6771\u4eac\u5927\u5b66 \u533b\u79d1\u5b66\u7814\u7a76\u6240)<\/p>\n\n\n\n<p><strong>PO-100<br><\/strong>Functional evaluation of the unknome in the most abundant marine bacterial clade<br>\u6d77\u6d0b\u306b\u512a\u5360\u3059\u308b\u7d30\u83cc\u7cfb\u7d71\u7fa4\u304c\u6301\u3064\u6a5f\u80fd\u672a\u77e5\u907a\u4f1d\u5b50\u306e\u7db2\u7f85\u7684\u306a\u6a5f\u80fd\u4e88\u6e2c\u89e3\u6790<br>\u897f\u91ce \u8061<sup>1,2<\/sup>, \u5bcc\u6c38 \u8ce2\u4eba<sup>1<\/sup>, \u5927\u524d \u516c\u4fdd<sup>3<\/sup>, \u6ff5\ufa11 \u6052\u4e8c<sup>1,2<\/sup>, \u897f\u6751 \u7950\u8cb4<sup>1<\/sup>, \u5409\u6fa4 \u664b<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66 \u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66 \u5927\u6c17\u6d77\u6d0b\u7814\u7a76\u6240, <sup>3<\/sup>\u7406\u7814 CPR)<\/p>\n\n\n\n<p><strong>PO-101<br><\/strong>Development of Microbial Species Distribution Models for Japanese Soils Using Citizen Science\u2013Based Environmental Metagenomic Data<br>\u5e02\u6c11\u79d1\u5b66\u7531\u6765\u306e\u74b0\u5883\u30e1\u30bf\u30b2\u30ce\u30e0\u30c7\u30fc\u30bf\u3092\u6d3b\u7528\u3057\u305f\u571f\u58cc\u5fae\u751f\u7269\u5206\u5e03\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9<br>\u9752\u6728 \u88d5\u4e00<sup>1,2<\/sup>, \u5927\u4e45\u4fdd \u667a\u53f8<sup>3<\/sup>, \u52a0\u85e4 \u5e83\u6d77<sup>3<\/sup>, \u4f50\u85e4 \u4fee\u6b63<sup>3<\/sup>, \u5357\u6fa4 \u7a76<sup>3 <\/sup>&nbsp;(<sup>1<\/sup>\u6771\u5317\u5927\u5b66\u3000\u6771\u5317\u30e1\u30c7\u30a3\u30ab\u30eb\u30fb\u30e1\u30ac\u30d0\u30f3\u30af\u6a5f\u69cb, <sup>2<\/sup>\u6771\u5317\u5927\u5b66\u3000\u5927\u5b66\u9662\u60c5\u5831\u79d1\u5b66\u7814\u7a76\u79d1, <sup>3<\/sup>\u6771\u5317\u5927\u5b66\u3000\u5927\u5b66\u9662\u751f\u547d\u79d1\u5b66\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-102<br><\/strong>PlanDyO: A Meta-Epigenomic Platform for Monitoring Marine Plankton Dynamics in Northeastern Japan<br>PlanDyO: \u6771\u5317\u6cbf\u5cb8\u57df\u306b\u304a\u3051\u308b\u6d77\u6d0b\u30d7\u30e9\u30f3\u30af\u30c8\u30f3\u52d5\u614b\u306e\u30e1\u30bf\u30a8\u30d4\u30b2\u30ce\u30e0\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0<br>\u5927\u6797 \u6b66<sup>1,2<\/sup>, \u85e4\u4e95 \u8c4a\u5c55<sup>1,3<\/sup>, \u5317\u6751 \u831c<sup>1<\/sup>, \u718a\u91ce \u5cb3<sup>1,4<\/sup>, \u6c60\u7530 \u5b9f<sup>1,4<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u5317\u5927\u5b66\u5909\u52d5\u6d77\u6d0b\u30a8\u30b3\u30b7\u30b9\u30c6\u30e0\u9ad8\u7b49\u7814\u7a76\u6240, <sup>2<\/sup>\u6771\u5317\u5927\u5b66\u5927\u5b66\u9662\u60c5\u5831\u79d1\u5b66\u7814\u7a76\u79d1, <sup>3<\/sup>\u6771\u5317\u5927\u5b66\u5927\u5b66\u9662\u8fb2\u5b66\u7814\u7a76\u79d1\u9644\u5c5e\u5973\u5ddd\u30d5\u30a3\u30fc\u30eb\u30c9\u30bb\u30f3\u30bf\u30fc, <sup>4<\/sup>\u6771\u5317\u5927\u5b66\u5927\u5b66\u9662\u751f\u547d\u79d1\u5b66\u7814\u7a76\u79d1\u9644\u5c5e\u6d45\u866b\u6d77\u6d0b\u751f\u7269\u5b66\u6559\u80b2\u7814\u7a76\u30bb\u30f3\u30bf\u30fc)<\/p>\n\n\n\n<p><strong>PO-103<br><\/strong>Tumor Microenvironment Subcellular Cell Interaction Analysis with a Multimodal&nbsp; Spatial Transcriptomics Deep Generative Model<br>\u816b\u760d\u5fae\u5c0f\u74b0\u5883\u7d30\u80de\u9593\u76f8\u4e92\u4f5c\u7528\u89e3\u6790\u306e\u305f\u3081\u306e\u30de\u30eb\u30c1\u30e2\u30fc\u30c0\u30eb\u7a7a\u9593\u30c8\u30e9\u30f3\u30b9\u30af\u30ea\u30d7\u30c8\u30fc\u30e0\u6df1\u5c64\u751f\u6210\u30e2\u30c7\u30eb<br>\u675c \u67cf\u92ed<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-104<br><\/strong>Integrative Spatial Transcriptomic and Geographical Analysis Reveals Cellular Co-localization Patterns in Hepatocellular Carcinoma<br>\u5730\u7406\u60c5\u5831\u89e3\u6790\u624b\u6cd5\u3092\u5fdc\u7528\u3057\u305f\u7a7a\u9593\u30c8\u30e9\u30f3\u30b9\u30af\u30ea\u30d7\u30c8\u30fc\u30e0\u89e3\u6790\u306b\u3088\u308b\u809d\u304c\u3093\u7d44\u7e54\u5185\u306e\u7d30\u80de\u5171\u5c40\u5728\u63a8\u5b9a<br>\u8d8a\u524d \u4f73\u5948\u6075<sup>1<\/sup>, \u91ce\u4e2d \u5141\u5e7e<sup>1<\/sup>, \u795e\u8c37 \u77e5\u61b2<sup>1<\/sup>, \u6b66\u85e4 \u82b3\u7f8e<sup>2<\/sup>, \u85e4\u4e95 \u82f1\u6a39<sup>2<\/sup>, \u5b5d\u6a4b \u8ce2\u4e00<sup>3<\/sup>, \u5c0f\u7389 \u5c1a\u5b8f<sup>4<\/sup>, \u5927\u8c37 \u76f4\u5b50<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u5927\u962a\u516c\u7acb\u5927\u5b66\u3000\u5927\u5b66\u9662\u533b\u5b66\u7814\u7a76\u79d1\u3000\u75c5\u614b\u751f\u7406\u5b66, <sup>2<\/sup>\u5927\u962a\u516c\u7acb\u5927\u5b66\u3000\u5927\u5b66\u9662\u533b\u5b66\u7814\u7a76\u79d1\u3000\u809d\u80c6\u81b5\u5185\u79d1\u5b66, <sup>3<\/sup>\u5927\u962a\u516c\u7acb\u5927\u5b66\u3000\u5927\u5b66\u9662\u533b\u5b66\u7814\u7a76\u79d1\u3000\u8a3a\u65ad\u75c5\u7406\u30fb\u75c5\u7406\u75c5\u614b\u5b66, <sup>4<\/sup>\u5927\u962a\u5927\u5b66\u3000\u5927\u5b66\u9662\u533b\u5b66\u7814\u7a76\u79d1\u3000\u6d88\u5316\u5668\u5185\u79d1\u5b66)<\/p>\n\n\n\n<p><strong>PO-105<br><\/strong>Spatial Gene Expression Estimation from Pathology Slides under Stochastic Noise and Batch Effect<br>\u30d0\u30c3\u30c1\u52b9\u679c\u3068\u78ba\u7387\u7684\u30ce\u30a4\u30ba\u3092\u6301\u3064\u7a7a\u9593\u30c8\u30e9\u30f3\u30b9\u30af\u30ea\u30d7\u30c8\u30df\u30af\u30b9\u306b\u304a\u3051\u308b\u76f8\u5bfe\u767a\u73fe\u5b66\u7fd2\u306b\u3088\u308b\u75c5\u7406\u753b\u50cf\u304b\u3089\u306e\u907a\u4f1d\u5b50\u767a\u73fe\u63a8\u5b9a<br>\u897f\u6751 \u548c\u4e5f<sup>1<\/sup>, \u5c0f\u5d8b \u6cf0\u5f18<sup>1<\/sup>, \u5ee3\u702c \u9065\u9999<sup>1<\/sup>, \u5099\u702c \u7adc\u99ac<sup>2<\/sup>, \u5fd7\u4e45 \u958b\u4eba<sup>2<\/sup>&nbsp; (<sup>1<\/sup>\u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc, <sup>2<\/sup>\u4e5d\u5dde\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-106<br><\/strong>Comprehensive analysis of breast cancer immune hot\/cold niches by spatial and transcriptome integrated analysis<br>\u7a7a\u9593\u30c8\u30e9\u30f3\u30b9\u30af\u30ea\u30d7\u30c8\u30fc\u30e0\u7d71\u5408\u89e3\u6790\u306b\u3088\u308b\u4e73\u304c\u3093\u514d\u75ab\u30db\u30c3\u30c8\uff0f\u30b3\u30fc\u30eb\u30c9\u30cb\u30c3\u30c1\u306e\u7db2\u7f85\u7684\u89e3\u6790<br>\u9ed2\u6728 \u5fc3\u548c<sup>1<\/sup>, \u662f\u679d \u9054\u4e5f<sup>2<\/sup>&nbsp; (<sup>1<\/sup>\u6a2a\u6d5c\u5e02\u7acb\u5927\u5b66\u5927\u5b66\u9662\u533b\u5b66\u7cfb\u7814\u7a76\u79d1, <sup>2<\/sup>\u30af\u30ea\u30cb\u30c3\u30af\u30d5\u30a9\u30a2\u7530\u753a)<\/p>\n\n\n\n<p><strong>PO-107<\/strong> \/ HT-302<strong><br><\/strong>MetDeeCINE: Deciphering Metabolic Regulation through Deep Learning and Multi-Omics<br>MetDeeCINE: \u5b9a\u91cf\u7684\u4ee3\u8b1d\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u7bc9\u306e\u305f\u3081\u306e\u30de\u30eb\u30c1\u30aa\u30df\u30af\u30b9\u7d71\u5408AI<br>\u4f0a\u6771 \u5de7<sup>1<\/sup>, \u5927\u91ce \u8061<sup>1<\/sup>, \u738b \u4e00\u7136<sup>2<\/sup>, \u9ed2\u7530 \u771f\u4e5f<sup>2<\/sup>, \u6e05\u6c34 \u79c0\u5e78<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-108<br><\/strong>Segmentation method of approximate cell region using a quadtree in spatial transcriptomics<br>\u7a7a\u9593\u30c8\u30e9\u30f3\u30b9\u30af\u30ea\u30d7\u30c8\u30fc\u30e0\u89e3\u6790\u306b\u304a\u3051\u308b\u9818\u57df\u56db\u5206\u6728\u3092\u7528\u3044\u305f\u7d30\u80de\u9818\u57df\u306e\u5206\u5272\u624b\u6cd5<br>\u76f8\u5ddd \u54f2\u54c9<sup>1<\/sup>, \u6749\u5c71 \u97ff<sup>1<\/sup>, \u7e41\u7530 \u6d69\u529f<sup>1<\/sup>, \u6885\u8c37 \u4fca\u6cbb<sup>1<\/sup>, \u702c\u5c3e \u8302\u4eba<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u5927\u962a\u5927\u5b66\u5927\u5b66\u9662\u60c5\u5831\u79d1\u5b66\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-109<br><\/strong>A computational approach to accurately predict metabolites of constituents in Kampo medicine.<br>\u6f22\u65b9\u85ac\u6210\u5206\u306e\u4ee3\u8b1d\u7523\u7269\u3092\u6b63\u78ba\u306b\u4e88\u6e2c\u3059\u308b\u8a08\u7b97\u30a2\u30d7\u30ed\u30fc\u30c1<br>\u6751\u6728 \u512a\u592a<sup>1,2<\/sup>, \u5927\u6e15 \u52dd\u4e5f<sup>2<\/sup>, \u897f \u660e\u7d00<sup>2<\/sup>, \u5c71\u897f \u82b3\u88d5<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u540d\u53e4\u5c4b\u5927\u5b66\u5927\u5b66\u9662,<sup> 2<\/sup>\u682a\u5f0f\u4f1a\u793e\u30c4\u30e0\u30e9)<\/p>\n\n\n\n<p><strong>PO-110<br><\/strong>Cell Level Analysis of Spatial Transcriptomics Using Visium HD<br>Visium HD\u306b\u3088\u308b\u7a7a\u9593\u30c8\u30e9\u30f3\u30b9\u30af\u30ea\u30d7\u30c8\u30fc\u30e0\u306e\u7d30\u80de\u5358\u4f4d\u89e3\u6790<br>\u5c71\u5d0e \u5c06\u592a\u6717<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u5927\u962a\u5927\u5b66\u30fb\u30d0\u30a4\u30aa\u30a4\u30f3\u30d5\u30a9\u30de\u30c6\u30a3\u30af\u30b9\u30bb\u30f3\u30bf\u30fc)<\/p>\n\n\n\n<p><strong>PO-111<br><\/strong>SpatialKNifeY (SKNY): Detection of the Tumor Microenvironment and Estimation of Tumor Progression Trajectory Using Spatial Transcriptomics of Breast Cancer<br>SpatialKNifeY (SKNY): \u4e73\u304c\u3093\u7a7a\u9593\u30c8\u30e9\u30f3\u30b9\u30af\u30ea\u30d7\u30c8\u30fc\u30e0\u3092\u7528\u3044\u305f\u816b\u760d\u5fae\u5c0f\u74b0\u5883\u306e\u691c\u51fa\u304a\u3088\u3073\u816b\u760d\u9032\u5c55\u306e\u63a8\u5b9a<br>\u9152\u4e95 \u4fca\u8f14<sup>1,2<\/sup>, \u91ce\u6751 \u4eae\u8f14<sup>1,3<\/sup>, \u6c60 \u6210\u57fa<sup>1<\/sup>, \u6c38\u6fa4 \u6167<sup>3,4<\/sup>, \u9234\u6728 \u7d62\u5b50<sup>3<\/sup>, \u9234\u6728 \u7a63<sup>3<\/sup>, \u77f3\u5ddd \u4fca\u5e73<sup>5<\/sup>, \u571f\u539f \u4e00\u54c9<sup>1,2<\/sup>, \u5f71\u5c71 \u4fca\u4e00\u90ce<sup>1<\/sup>, \u5c71\u4e0b \u7406\u5b87<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc \u5148\u7aef\u533b\u7642\u958b\u767a\u30bb\u30f3\u30bf\u30fc \u30c8\u30e9\u30f3\u30b9\u30ec\u30fc\u30b7\u30e7\u30ca\u30eb\u30a4\u30f3\u30d5\u30a9\u30de\u30c6\u30a3\u30af\u30b9\u5206\u91ce, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662 \u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1 \u5148\u7aef\u751f\u547d\u79d1\u5b66\u5c02\u653b, <sup>3<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662 \u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1 \u30e1\u30c7\u30a3\u30ab\u30eb\u60c5\u5831\u751f\u547d\u5c02\u653b, <sup>4<\/sup>\u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u6771\u75c5\u9662 \u4e73\u817a\u5916\u79d1, <sup>5<\/sup>\u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc \u5148\u7aef\u533b\u7642\u958b\u767a\u30bb\u30f3\u30bf\u30fc \u81e8\u5e8a\u816b\u760d\u75c5\u7406\u5206\u91ce)<\/p>\n\n\n\n<p><strong>PO-112<br><\/strong>Improving miRNA Single-Molecule Detection via Ambiguity Reduction in Nanopore Signal Data<br>\u30ca\u30ce\u30dd\u30a2\u8a08\u6e2c\u30c7\u30fc\u30bf\u304b\u3089\u306e\u66d6\u6627\u4fe1\u53f7\u9664\u53bb\u306b\u3088\u308b microRNA \u5358\u5206\u5b50\u8b58\u5225\u7cbe\u5ea6\u306e\u5411\u4e0a<br>\u6c5f\u6751 \u8061\u99ac<sup>1<\/sup>, \u7af9\u5185 \u4e03\u6d77<sup>1<\/sup>, \u5ddd\u91ce \u7adc\u53f8<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u8fb2\u5de5\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-113<br><\/strong>DeepSpaceDB: a spatial transcriptomics atlas covering a wide variety of tissues and conditions<br>DeepSpaceDB\uff1a\u591a\u69d8\u306a\u7d44\u7e54\u30fb\u75be\u60a3\u3092\u7db2\u7f85\u3059\u308b\u7a7a\u9593\u30c8\u30e9\u30f3\u30b9\u30af\u30ea\u30d7\u30c8\u30df\u30af\u30b9\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9<br>Vandenbon Alexis<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u4eac\u90fd\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-114<br><\/strong>Spatially-Informed Ranker for Disease-associated gene Prioritization<br>\u75be\u60a3\u95a2\u9023\u907a\u4f1d\u5b50\u306e\u512a\u5148\u9806\u4f4d\u4ed8\u3051\u306e\u305f\u3081\u306e\u7a7a\u9593\u60c5\u5831\u6d3b\u7528\u30e9\u30f3\u30ab\u30fc<br>\u5f35 \u6d2a\u745e<sup>1<\/sup>, \u7b20\u539f \u96c5\u5f18<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662\u3000\u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1\u3000\u30e1\u30c7\u30a3\u30ab\u30eb\u60c5\u5831\u751f\u547d\u5c02\u653b, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662\u3000\u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1\u3000\u751f\u547d\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u30bb\u30f3\u30bf\u30fc)<\/p>\n\n\n\n<p><strong>PO-115<br><\/strong>HPC cluster construction and RNA-seq analysis pipeline<br>HPC\u30af\u30e9\u30b9\u30bf\u69cb\u7bc9\u3068RNA-seq\u89e3\u6790\u30d1\u30a4\u30d7\u30e9\u30a4\u30f3<br>\u52a0\u85e4 \u5927\u548c<sup>1<\/sup>, \u77f3\u5ddd \u660c\u548c<sup>1<\/sup>, \u6851\u539f \u77e5\u5df3<sup>1,2<\/sup>, \u79cb\u5149 \u548c\u4e5f<sup>1,3<\/sup>&nbsp; (<sup>1<\/sup>\u9999\u5ddd\u5927\u5b66\u3000\u30d0\u30a4\u30aa\u30a4\u30f3\u30d5\u30a9\u30de\u30c6\u30a3\u30af\u30b9\u89e3\u6790\u30bb\u30f3\u30bf\u30fc, <sup>2<\/sup>\u9999\u5ddd\u5927\u5b66\u3000\u533b\u5b66\u90e8, <sup>3<\/sup>\u9999\u5ddd\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-116<br><\/strong>Computational Modeling of Synaptic Pruning in Neural Circuit Formation<br>\u795e\u7d4c\u56de\u8def\u5f62\u6210\u306b\u304a\u3051\u308b\u30b7\u30ca\u30d7\u30b9\u5208\u308a\u8fbc\u307f\u306e\u6570\u7406\u30e2\u30c7\u30eb\u69cb\u7bc9<br>\u5b87\u6cbb\u5ddd \u548c\u5e0c<sup>1<\/sup>, \u6ce2\u6c5f\u91ce \u6d0b<sup>2<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u7406\u79d1\u5927\u5b66\u5927\u5b66\u9662\u751f\u547d\u79d1\u5b66\u7814\u7a76\u79d1, <sup>2<\/sup>\u6771\u4eac\u7406\u79d1\u5927\u5b66\u751f\u547d\u533b\u79d1\u5b66\u7814\u7a76\u6240)<\/p>\n\n\n\n<p><strong>PO-117<br><\/strong>Exploration of chemical characteristics associated with the uncertainty in BCF prediction<br>BCF \u4e88\u6e2c\u306e\u4e0d\u78ba\u5b9f\u6027\u306b\u95a2\u4e0e\u3059\u308b\u5316\u5b66\u7269\u8cea\u306e\u7279\u5fb4\u63a2\u7d22<br>\u524d\u7530 \u9678<sup>2<\/sup>, \u4ef2\u5c71 \u6176<sup>1<\/sup>, \u7530\u4e0a \u7460\u7f8e<sup>1<\/sup>, \u98ef\u7530 \u7dd1<sup>2 <\/sup>&nbsp;(<sup>1<\/sup>\u611b\u5a9b\u5927\u5b66 \u6cbf\u5cb8\u74b0\u5883\u79d1\u5b66\u7814\u7a76\u30bb\u30f3\u30bf\u30fc, <sup>2<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66\u3000\u60c5\u5831\u5de5\u5b66\u90e8)<\/p>\n\n\n\n<p><strong>PO-118<br><\/strong>Developing Explainable BCF Prediction Models for Accelerating Environmental Risk Assessment<br>\u74b0\u5883\u30ea\u30b9\u30af\u8a55\u4fa1\u306e\u52b9\u7387\u5316\u306b\u5411\u3051\u305f\u89e3\u91c8\u53ef\u80fd\u306aBCF\u4e88\u6e2c\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9<br>\u5409\u6751 \u6cf0\u5fd7<sup>1<\/sup>, \u4ef2\u5c71&nbsp; \u6176<sup>2<\/sup>, \u7530\u4e0a \u7460\u7f8e<sup>2<\/sup>, \u98ef\u7530 \u7dd1<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66\u3000\u60c5\u5831\u5de5\u5b66\u90e8, <sup>2<\/sup>\u611b\u5a9b\u5927\u5b66\u3000\u6cbf\u5cb8\u74b0\u5883\u79d1\u5b66\u7814\u7a76\u30bb\u30f3\u30bf\u30fc)<\/p>\n\n\n\n<p><strong>PO-119<br><\/strong>Facilitating International Collaboration and Utilization through the Japanese Translation of Phenotype Ontologies<br>\u8868\u73fe\u578b\u30aa\u30f3\u30c8\u30ed\u30b8\u30fc\uff08MONDO\/MP\uff09\u306e\u65e5\u672c\u8a9e\u5316\u306b\u3088\u308b\u56fd\u969b\u9023\u643a\u3068\u6d3b\u7528<br>\u9ad8\u6708 \u7167\u6c5f<sup>1<\/sup>, \u85e4\u539f \u8c4a\u53f2<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u5927\u5b66\u5171\u540c\u5229\u7528\u6a5f\u95a2\u6cd5\u4eba \u60c5\u5831\u30fb\u30b7\u30b9\u30c6\u30e0\u7814\u7a76\u6a5f\u69cb&nbsp; \u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u5171\u540c\u5229\u7528\u57fa\u76e4\u65bd\u8a2d&nbsp; \u30e9\u30a4\u30d5\u30b5\u30a4\u30a8\u30f3\u30b9\u7d71\u5408\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30bb\u30f3\u30bf\u30fc)<\/p>\n\n\n\n<p><strong>PO-120<br><\/strong>Prediction of Kampo medicines to reduce side effects caused by Western medicines<br>\u897f\u6d0b\u85ac\u306b\u3088\u308b\u526f\u4f5c\u7528\u3092\u8efd\u6e1b\u3059\u308b\u6f22\u65b9\u85ac\u306e\u4e88\u6e2c<br>\u4e0a\u7530 \u307f\u306e\u308a<sup>1<\/sup>, \u4e00\u30ce\u702c \u97f3\u8449<sup>1<\/sup>, \u4e80\u6df5 \u7531\u4e43<sup>1<\/sup>, \u5cf6\u7530 \u7950\u6a39<sup>1<\/sup>, \u6fa4\u7530 \u9686\u4ecb<sup>2<\/sup>, \u9580\u8107 \u771f<sup>3<\/sup>, \u5c71\u897f \u82b3\u88d5<sup>4<\/sup>, \u5ca9\u7530 \u901a\u592b<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66, <sup>2<\/sup>\u5ca1\u5c71\u5927\u5b66, <sup>3<\/sup>\u5bcc\u5c71\u5927\u5b66, <sup>4<\/sup>\u540d\u53e4\u5c4b\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-121<br><\/strong>Study on Prediction Models of Protein-Protein Interactions in Hetero Protein Complexes using ESM-C<br>ESM-C\u3092\u7528\u3044\u305f\u30d8\u30c6\u30ed\u30bf\u30f3\u30d1\u30af\u8907\u5408\u4f53\u306e\u76f8\u4e92\u4f5c\u7528\u90e8\u4f4d\u4e88\u6e2c\u30e2\u30c7\u30eb\u306e\u691c\u8a0e<br>\u674e \u5609\u4e00<sup>2<\/sup>, \u7c9f\u91ce \u6d69\u5927<sup>1<\/sup>, \u7530\u4e2d \u5553\u4ecb<sup>1<\/sup>, \u6751\u4e0a \u6d0b\u4e00<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u60c5\u5831\u5927\u5b66\u7dcf\u5408\u60c5\u5831\u5b66\u90e8, <sup>2<\/sup>\u6771\u4eac\u60c5\u5831\u5927\u5b66\u7dcf\u5408\u60c5\u5831\u5b66\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-122<br><\/strong>Impact of Geohistorical Oxygen Levels on Globin Molecular Evolution: A Phylogenetic Approach<br>\u53e4\u4ee3\u306e\u9178\u7d20\u6fc3\u5ea6\u304c\u30b0\u30ed\u30d3\u30f3\u306e\u5206\u5b50\u9032\u5316\u306b\u4e0e\u3048\u305f\u5f71\u97ff\uff1a\u7cfb\u7d71\u89e3\u6790\u7684\u624b\u6cd5\u306b\u3088\u308b\u691c\u8a0e<br>\u68ee\u7530 \u661f\u7f85<sup>1<\/sup>, \u5927\u68ee \u8061<sup>1<\/sup>, \u4eca\u6751 \u6bd4\u5442\u5fd7<sup>1<\/sup>, \u58a8 \u667a\u6210<sup>2<\/sup>, \u5e38\u91cd \u30a2\u30f3\u30c8\u30cb\u30aa<sup>3<\/sup>, \u78ef\u8c9d \u6cf0\u5f18<sup>4<\/sup>, \u767d\u4e95 \u525b<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u9577\u6d5c\u30d0\u30a4\u30aa\u5927\u5b66, <sup>2<\/sup>\u5ba4\u862d\u5de5\u696d\u5927\u5b66, <sup>3<\/sup>\u6cd5\u653f\u5927\u5b66, <sup>4<\/sup>\u5bcc\u5c71\u770c\u7acb\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-123<br><\/strong>A fast method for calculating haplotype frequencies using Wavelet Tree<br>Wavelet Tree\u3092\u7528\u3044\u305f\u9ad8\u901f\u306a\u30cf\u30d7\u30ed\u30bf\u30a4\u30d7\u983b\u5ea6\u8a08\u7b97\u6cd5<br>\u4e09\u6fa4 \u8a08\u6cbb<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6a2a\u6d5c\u5e02\u7acb\u5927\u5b66\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u5b66\u90e8)<\/p>\n\n\n\n<p><strong>PO-124<br><\/strong>DiCleavePlus: A Transformer-based model to detect Dicer<br>DiCleavePlus\uff1a\u30d1\u30bf\u30fc\u30f3\u4e2d\u306eDicer\u5207\u65ad\u30b5\u30a4\u30c8\u3092\u691c\u51fa\u3059\u308bTransformer\u30d9\u30fc\u30b9\u30e2\u30c7\u30eb<br>\u725f \u674e\u7384<sup>1<\/sup>, \u963f\u4e45\u6d25 \u9054\u4e5f<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u4eac\u90fd\u5927\u5b66\u5316\u5b66\u7814\u7a76\u6240\u30d0\u30a4\u30aa\u30a4\u30f3\u30d5\u30a9\u30de\u30c6\u30a3\u30af\u30b9\u30bb\u30f3\u30bf\u30fc)<\/p>\n\n\n\n<p><strong>PO-125<br><\/strong>Full-length transcriptome profiling uncovers extensive isoform alterations during human somatic cell reprogramming<br>\u30d5\u30eb\u30ec\u30f3\u30b0\u30b9RNA\u30d7\u30ed\u30d5\u30a1\u30a4\u30ea\u30f3\u30b0\u306b\u3088\u308a\u660e\u3089\u304b\u306b\u306a\u3063\u305f\u30d2\u30c8\u4f53\u7d30\u80de\u521d\u671f\u5316\u306b\u304a\u3051\u308b\u5927\u898f\u6a21\u306a\u30a2\u30a4\u30bd\u30d5\u30a9\u30fc\u30e0\u306e\u518d\u7de8\u6210<br>\u6b63\u4e95 \u8061\u7f8e<sup>1,2<\/sup>, \u67da\u6728 \u5eb7\u5f18<sup>1<\/sup>, \u65bd \u5049\u9d6c<sup>1,2<\/sup>, \u6cb3\u53e3 \u7406\u7d17<sup>1,3<\/sup>, \u4e2d\u5ddd \u8aa0\u4eba<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u4eac\u90fd\u5927\u5b66iPS\u7d30\u80de\u7814\u7a76\u6240, <sup>2<\/sup>\u4eac\u90fd\u5927\u5b66\u5927\u5b66\u9662\u533b\u5b66\u7814\u7a76\u79d1, <sup>3<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662\u85ac\u5b66\u7cfb\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-126<br><\/strong>Improving Cell Type Homogeneity in Single-Cell Clustering by Leveraging Supervised Cell Type Classification Model<br>\u6559\u5e2b\u3042\u308a\u7d30\u80de\u7a2e\u63a8\u5b9a\u30e2\u30c7\u30eb\u3092\u6d3b\u7528\u3057\u305f\u5358\u4e00\u7d30\u80de\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306e\u5747\u4e00\u6027\u5411\u4e0a<br>\u68ee\u4e0b \u7dcf\u53f8<sup>1<\/sup>, \u5409\u7530 \u771f\u5e0c\u5b50<sup>1<\/sup>, \u5b89\u7530 \u77e5\u5f18<sup>1<\/sup>, \u767d\u4e95 \u6b63\u656c<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u682a\u5f0f\u4f1a\u793e\u65e5\u7acb\u88fd\u4f5c\u6240)<\/p>\n\n\n\n<p><strong>PO-127<br><\/strong>Long-Read Sequencing of Aquatic Environmental DNA for Complete Mitogenome Reconstruction<br>\u30ed\u30f3\u30b0\u30ea\u30fc\u30c9\u30b7\u30fc\u30b1\u30f3\u30b7\u30f3\u30b0\u306b\u3088\u308b\u74b0\u5883DNA\u304b\u3089\u306e\u30df\u30c8\u30b3\u30f3\u30c9\u30ea\u30a2\u5168\u9577\u306e\u7372\u5f97<br>\u6c34\u91ce \u3072\u306a\u306e<sup>1<\/sup>, \u7530\u4e2d \u79c0\u5178<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u682a\u5f0f\u4f1a\u793e\u8c4a\u7530\u4e2d\u592e\u7814\u7a76\u6240)<\/p>\n\n\n\n<p><strong>PO-128<br><\/strong>Single-cell analysis tracing karyotypic and transcriptional evolution of aneuploid cells<br>\u30b7\u30f3\u30b0\u30eb\u30bb\u30eb\u89e3\u6790\u304c\u660e\u304b\u3059\u304c\u3093\u7d30\u80de\u306e\u9032\u5316\u904e\u7a0b\uff1a\u67d3\u8272\u4f53\u306e\u7570\u6570\u6027\u3068\u8ee2\u5199\u5909\u5316\u306e\u8ffd\u8de1<br>\u96f7 \u58f0\u8d8a<sup>1,2<\/sup>, \u8d99 \u6c11\u77e5<sup>1<\/sup>, \u52a0\u85e4 \u8a69\u5b50<sup>1<\/sup>, \u5e83\u7530 \u4ea8<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u516c\u76ca\u8ca1\u56e3\u6cd5\u4eba\u304c\u3093\u7814\u7a76\u4f1a\u30fb\u304c\u3093\u7814\u7a76\u6240\u30fb\u5b9f\u9a13\u75c5\u7406\u90e8, <sup>2<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66\u5927\u5b66\u9662\u30fbJFCR\u816b\u760d\u5236\u5fa1\u5b66)<\/p>\n\n\n\n<p><strong>PO-129<br><\/strong>Exon-Specific DNA Methylation Patterns in the Mouse Hippocampus<br>\u30de\u30a6\u30b9\u6d77\u99ac\u306b\u304a\u3051\u308b\u30a8\u30af\u30bd\u30f3\u7279\u7570\u7684DNA\u30e1\u30c1\u30eb\u5316<br>Ganchimeg Namuunbayar<sup>1<\/sup>, Katsuya Uchida<sup>1<\/sup>, Akane Kitamura<sup>1<\/sup>, Takeshi Obayashi<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u5317\u5927\u5b66\u5927\u5b66\u9662 \u5927\u5b66\u9662\u60c5\u5831\u79d1\u5b66\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-130<br><\/strong>Proposing a Sampling Method for Sequence Alignments with GFlowNets<br>GFlowNets\u3092\u7528\u3044\u305f\u914d\u5217\u30a2\u30e9\u30a4\u30f3\u30e1\u30f3\u30c8\u624b\u6cd5\u306e\u63d0\u6848<br>\u5c71\u5cb8 \u512a\u5e0c<sup>1<\/sup>, \u798f\u6c38 \u6d25\u5d69<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6176\u61c9\u7fa9\u587e\u5927\u5b66\u7406\u5de5\u5b66\u90e8\u751f\u547d\u60c5\u5831\u5b66\u79d1)<\/p>\n\n\n\n<p><strong>PO-131<br><\/strong>GEMS: Finite-State Machine Abstraction for General Laboratory Automation<br>GEMS: \u5b9f\u9a13\u306e\u6c4e\u7528\u81ea\u52d5\u5316\u306b\u5411\u3051\u305f\u6709\u9650\u72b6\u614b\u6a5f\u68b0\u306e\u5c0e\u5165<br>\u7530\u539f-\u65b0\u4e95 \u60a0\u4e5f<sup>2<\/sup>, \u52a0\u85e4 \u6708<sup>1<\/sup>, \u843d\u5408 \u5e78\u6cbb<sup>1<\/sup>, \u963f\u4f4f \u548c\u54c9<sup>1<\/sup>, \u795e\u7530 \u5143\u7d00<sup>3<\/sup>, \u5c3e\u5d0e \u907c<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u7406\u5316\u5b66\u7814\u7a76\u6240, <sup>2<\/sup>\u7b51\u6ce2\u5927\u5b66, <sup>3<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-132<br><\/strong>Streamlined scRNA-seq Workflows with nf-core: A Nextflow-Based Solution for Large-Scale Data Integration<br>nf-core\u306b\u3088\u308bscRNA-seq\u30ef\u30fc\u30af\u30d5\u30ed\u30fc\u306e\u52b9\u7387\u5316\uff1a\u5927\u898f\u6a21\u30c7\u30fc\u30bf\u7d71\u5408\u306e\u305f\u3081\u306eNextflow\u30d9\u30fc\u30b9\u306e\u30bd\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3<br>\u30ca\u30ac\u30a4 \u30eb\u30a4\u30b9<sup>1,2<\/sup>, Trummer Nico<sup>2<\/sup>, Reif Serafina<sup>2<\/sup>, Dietrich Alexander<sup>2<\/sup>, Hafner Leon<sup>2<\/sup>, Weyrich Malte<sup>2<\/sup>, Nakato Ryuichiro<sup>1<\/sup>, List Markus<sup>2<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66, <sup>2<\/sup>Technical University of Munich)<\/p>\n\n\n\n<p><strong>PO-133<br><\/strong>Design of de novo nanopores via structure prediction and classification models<br>\u69cb\u9020\u4e88\u6e2c\u3068\u5206\u985e\u30e2\u30c7\u30eb\u3092\u7d44\u307f\u5408\u308f\u305b\u305fde novo\u30ca\u30ce\u30dd\u30a2\u8a2d\u8a08<br>\u4f50\u85e4 \u8309\u5948<sup>1<\/sup>, \u85e4\u7530 \u7965\u5b50<sup>1<\/sup>, \u5ddd\u91ce \u7adc\u53f8<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u8fb2\u5de5\u5927\u5b66\u3000\u5de5\u5b66\u5e9c\u3000\u751f\u547d\u5de5\u5b66\u5c02\u653b)<\/p>\n\n\n\n<p><strong>PO-134<br><\/strong>A hybrid strategy combining deep learning and MD simulation to design BCAT1-inhibitory proteins<br>\u6df1\u5c64\u5b66\u7fd2\u3068MD\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3092\u7d44\u307f\u5408\u308f\u305b\u305fBCAT1\u963b\u5bb3\u30bf\u30f3\u30d1\u30af\u8cea\u8a2d\u8a08\u306e\u305f\u3081\u306e\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9\u6226\u7565<br>\u6797 \u5468\u6597<sup>1,2<\/sup>, \u6797 \u5eb7\u8cb4<sup>1<\/sup>, \u5c0f\u95a2 \u6e96<sup>3<\/sup>, \u5cf6\u6751 \u5fb9\u5e73<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66, <sup>2<\/sup>\u30ca\u30ce\u533b\u7642\u30a4\u30ce\u30d9\u30fc\u30b7\u30e7\u30f3\u30bb\u30f3\u30bf\u30fc, <sup>3<\/sup>\u7523\u696d\u6280\u8853\u7dcf\u5408\u7814\u7a76\u6240)<\/p>\n\n\n\n<p><strong>PO-135<br><\/strong>Comprehensive analysis of genome variants affecting antibody-drug binding affinity using complex structure prediction AI<br>\u6297\u4f53\u2015\u6297\u539f\u8907\u5408\u4f53\u69cb\u9020\u4e88\u6e2cAI\u3092\u6d3b\u7528\u3057\u305f\u7d50\u5408\u89aa\u548c\u6027\u306b\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u30b2\u30ce\u30e0\u30d0\u30ea\u30a2\u30f3\u30c8\u306e\u5305\u62ec\u7684\u89e3\u6790<br>\u571f\u65b9 \u6566\u53f8<sup>2<\/sup>, \u6751\u5c71 \u6075\u4e00\u90ce<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u85ac\u5927\u30fb\u9662\u751f\u547d, <sup>2<\/sup>\u6771\u85ac\u5927\u30fb\u751f\u547d)<\/p>\n\n\n\n<p><strong>PO-136<br><\/strong>A hybrid approach for RNA 3D structure prediction integrating deep learning and simulation<br>\u6df1\u5c64\u5b66\u7fd2\u3068\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3092\u7528\u3044\u305fRNA3\u6b21\u5143\u69cb\u9020\u306e\u4e88\u6e2c<br>\u7bc9\u5c71 \u7fd4<sup>1<\/sup>, \u7bc9\u5c71\u3000\u7fd4<sup>1<\/sup>, \u5009\u7530\u3000\u535a\u4e4b<sup>2<\/sup>, \u4f50\u85e4\u3000\u5065\u543e<sup>1<\/sup>, Yang Zhang<sup>3<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66, <sup>2<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66, <sup>3<\/sup>\u30b7\u30f3\u30ac\u30dd\u30fc\u30eb\u56fd\u7acb\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-137<br><\/strong>Score Distribution Learning for Predicting Diverse Bioactivity Values in Protein-Ligand Docking for GPCRs and Kinases<br>GPCR\u304a\u3088\u3073\u30ad\u30ca\u30fc\u30bc\u3092\u5bfe\u8c61\u3068\u3057\u305f\u30bf\u30f3\u30d1\u30af\u8cea-\u30ea\u30ac\u30f3\u30c9\u30c9\u30c3\u30ad\u30f3\u30b0\u306b\u304a\u3051\u308b\u591a\u69d8\u306a\u751f\u7269\u6d3b\u6027\u5024\u4e88\u6e2c\u306e\u305f\u3081\u306e\u30b9\u30b3\u30a2\u5206\u5e03\u5b66\u7fd2<br>\u53f2 \u4eac\u5dde<sup>2<\/sup>, \u6e21\u8fba \u4f73\u6643<sup>1,2<\/sup>, \u5ca9\u8218 \u6e80\u96c4<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u4e2d\u592e\u5927\u5b66\u7406\u5de5\u5b66\u90e8\u751f\u547d\u79d1\u5b66\u79d1, <sup>2<\/sup>\u4e2d\u592e\u5927\u5b66\u5927\u5b66\u9662\u7406\u5de5\u5b66\u7814\u7a76\u79d1\u751f\u547d\u79d1\u5b66\u5c02\u653b)<\/p>\n\n\n\n<p><strong>PO-138<br><\/strong>A graph representation of probe spheres and amino acids for interacting molecule prediction from 3D protein structure<br>\u7acb\u4f53\u69cb\u9020\u304b\u3089\u76f8\u4e92\u4f5c\u7528\u5206\u5b50\u3092\u4e88\u6e2c\u3059\u308b\u305f\u3081\u306e\u30d7\u30ed\u30fc\u30d6\u7403\u7fa4\u3068\u30a2\u30df\u30ce\u9178\u7fa4\u3092\u30ce\u30fc\u30c9\u3068\u3059\u308b\u30b0\u30e9\u30d5\u8868\u73fe<br>\u5ddd\u7aef \u731b<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u5317\u5927\u5b66\u3000\u5927\u5b66\u9662\u60c5\u5831\u79d1\u5b66\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-139<br><\/strong>Co-transcriptional folding simulation of tandemly arranged artificial riboswitches<br>\u30bf\u30f3\u30c7\u30e0\u914d\u7f6e\u3055\u308c\u305f\u4eba\u5de5\u30ea\u30dc\u30b9\u30a4\u30c3\u30c1\u306e\u8ee2\u5199\u4e2d\u30d5\u30a9\u30fc\u30eb\u30c7\u30a3\u30f3\u30b0\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3<br>\u7a2e\u7530 \u6643\u4eba<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u5f18\u524d\u5927\u5b66\u5927\u5b66\u9662\u7406\u5de5\u5b66\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-140<br><\/strong>Simulation analysis of visual functions of voltage-gated Sodium channels expressed in human photoreceptor cells<br>\u30d2\u30c8\u8996\u7d30\u80de\u306b\u767a\u73fe\u3059\u308b\u819c\u96fb\u4f4d\u4f9d\u5b58\u6027Na+\u30c1\u30e3\u30cd\u30eb\u306e\u8996\u899a\u6a5f\u80fd\u306e\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u89e3\u6790<br>\u6cb3\u5408 \u623f\u592b<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u85e4\u7530\u533b\u79d1\u5927\u5b66\u533b\u5b66\u90e8\u751f\u7406\u5b66II)<\/p>\n\n\n\n<p><strong>PO-141<br><\/strong>Heart rate variability analysis of the effects of odorants on human autonomic nervous activity<br>\u30d2\u30c8\u81ea\u5f8b\u795e\u7d4c\u6d3b\u52d5\u306b\u5302\u3044\u7269\u8cea\u304c\u53ca\u307c\u3059\u5f71\u97ff\u306e\u5fc3\u62cd\u5909\u52d5\u89e3\u6790<br>\u6cb3\u5408 \u623f\u592b<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u85e4\u7530\u533b\u79d1\u5927\u5b66\u533b\u5b66\u90e8\u751f\u7406\u5b66II)<\/p>\n\n\n\n<p><strong>PO-142<br><\/strong>Enhancing TCR-pMHC II Binding Prediction with Contrastive Learning<br>\u5bfe\u7167\u5b66\u7fd2\u3092\u6d3b\u7528\u3057\u305fTCR-pMHC\u30af\u30e9\u30b9II\u7d50\u5408\u4e88\u6e2cAI\u30e2\u30c7\u30eb\u306e\u958b\u767a<br>\u4e2d\u897f \u4e00\u8cb4<sup>1<\/sup>, \u6e05\u6c34 \u79c0\u5e78<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-143<br><\/strong>Chaotic Search-Enhanced Real-Coded Genetic Algorithm<br>\u4ee3\u8b1d\u30e2\u30c7\u30eb\u306e\u6700\u9069\u5316\u3092\u76ee\u7684\u3068\u3059\u308b\u30ab\u30aa\u30b9\u63a2\u7d22\u3092\u5c0e\u5165\u3057\u305f\u5b9f\u6570\u578b\u907a\u4f1d\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0<br>\u795e\u7530 \u88d5\u4e5f<sup>1<\/sup>, \u9060\u91cc \u7531\u4f73\u5b50<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u7acb\u547d\u9928\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-144<br><\/strong>CoSiNe and SignedLFR: Tools for Signed Graph Simulation and Gene Community Detection in Single-Cell RNA-seq Data<br>CoSiNe\u3068SignedLFR\uff1a\u5358\u4e00\u7d30\u80deRNA-seq\u30c7\u30fc\u30bf\u306b\u304a\u3051\u308b\u30b5\u30a4\u30f3\u4ed8\u304d\u30b0\u30e9\u30d5\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3068\u907a\u4f1d\u5b50\u30b3\u30df\u30e5\u30cb\u30c6\u30a3\u691c\u51fa\u306e\u305f\u3081\u306e\u30c4\u30fc\u30eb<br>\u30ca\u30ac\u30a4 \u30eb\u30a4\u30b9<sup>1,2<\/sup>, List Markus1, Nakato Ryuichiro<sup>2<\/sup>&nbsp; (<sup>1<\/sup>Technical University of Munich, <sup>2<\/sup>university of tokyo)<\/p>\n\n\n\n<p><strong>PO-145<br><\/strong>INGOR: A Multi-purpose Biological Network Estimation Application<br>INGOR: \u591a\u76ee\u7684\u751f\u7269\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u4e88\u6e2c\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3<br>\u7389\u7530 \u5609\u7d00<sup>1<\/sup>, \u4e2d\u6fa4 \u9ebb\u8863<sup>1,2<\/sup>, \u85e4\u672c \u5065\u4e8c<sup>3<\/sup>, \u6afb\u6728 \u5b9f<sup>2<\/sup>, \u5cf0\u6674 \u967d\u5e73<sup>2<\/sup>, \u5185\u91ce \u8a60\u4e00\u90ce<sup>2<\/sup>, \u5965\u91ce \u606d\u53f2<sup>2 <\/sup>&nbsp;(<sup>1<\/sup>\u5f18\u524d\u5927\u5b66\u5927\u5b66\u9662\u533b\u5b66\u7814\u7a76\u79d1\u9644\u5c5e\u5065\u5eb7\u30fb\u533b\u7642\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u7814\u7a76\u30bb\u30f3\u30bf\u30fc, <sup>2<\/sup>\u4eac\u90fd\u5927\u5b66\u5927\u5b66\u9662\u533b\u5b66\u7814\u7a76\u79d1\u30d3\u30c3\u30b0\u30c7\u30fc\u30bf\u533b\u79d1\u5b66\u5206\u91ce, <sup>3<\/sup>\u5f18\u524d\u5927\u5b66\u5927\u5b66\u9662\u7406\u5de5\u5b66\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-146<br><\/strong>Neural posterior estimation for switching stochastic differential equation-based models applied to biological time-series data<br>\u751f\u547d\u60c5\u5831\u30c7\u30fc\u30bf\u306b\u304a\u3051\u308b\u30b9\u30a4\u30c3\u30c1\u30f3\u30b0\u78ba\u7387\u5fae\u5206\u65b9\u7a0b\u5f0f\u30d9\u30fc\u30b9\u30e2\u30c7\u30eb\u3078\u306e\u30cb\u30e5\u30fc\u30e9\u30eb\u4e8b\u5f8c\u63a8\u5b9a\u306e\u9069\u7528<br>\u7d30\u7530 \u81f3\u6e29<sup>1<\/sup>, \u6d5c\u7530 \u9053\u662d<sup>1,2,3<\/sup>&nbsp; (<sup>1<\/sup>\u65e9\u7a32\u7530\u5927\u5b66, <sup>2<\/sup>\u7523\u696d\u6280\u8853\u7dcf\u5408\u7814\u7a76\u6240, <sup>3<\/sup>\u65e5\u672c\u533b\u79d1\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-147<br><\/strong>Predicting reprogramming factors based on gene regulatory dynamics<br>\u907a\u4f1d\u5b50\u5236\u5fa1\u30c0\u30a4\u30ca\u30df\u30af\u30b9\u306b\u57fa\u3065\u304f\u30ea\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u56e0\u5b50\u4e88\u6e2c<br>\u77f3\u5ddd \u96c5\u4eba<sup>1<\/sup>, \u671b\u6708 \u6566\u53f2<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u4eac\u90fd\u5927\u5b66\u3000\u533b\u751f\u7269\u5b66\u7814\u7a76\u6240)<\/p>\n\n\n\n<p><strong>PO-148<\/strong> \/ HT-101<br>Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction<br>\u30bf\u30f3\u30d1\u30af\u8cea\u9593\u76f8\u4e92\u4f5c\u7528\u3092\u5229\u7528\u3057\u305f\u4eba\u5de5\u77e5\u80fd\u306b\u3088\u308b\u65b0\u3057\u3044\u85ac\u5264\u907a\u4f1d\u5b50-\u75be\u60a3\u76f8\u4e92\u4f5c\u7528\u306e\u540c\u5b9a<br>\u7530\u53e3 \u5584\u5f18<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u4e2d\u592e\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-149<br><\/strong>Novel AI-powered computational method using tensor decomposition can discover the common optimal bin sizes when integrating multiple Hi-C datasets<br>\u30c6\u30f3\u30bd\u30eb\u5206\u89e3\u3092\u7528\u3044\u305f\u65b0\u898f\u306eAI\u8a08\u7b97\u624b\u6cd5\u306f\u3001\u8907\u6570\u306eHi-C\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u7d71\u5408\u3059\u308b\u969b\u306b\u3001\u5171\u901a\u306e\u6700\u9069\u306a\u30d3\u30f3\u30b5\u30a4\u30ba\u3092\u767a\u898b\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b<br>\u7530\u53e3 \u5584\u5f18<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u4e2d\u592e\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-150<br><\/strong>MORE-RNAseq: a pipeline for quantifying retrotransposition-capable LINE1 expression based on RNA-seq data<br>MORE-RNAseq: RNA-seq\u30c7\u30fc\u30bf\u304b\u3089\u8ee2\u79fb\u53ef\u80fd\u306aLINE1\u767a\u73fe\u3092\u540c\u5b9a\u3059\u308b\u30d1\u30a4\u30d7\u30e9\u30a4\u30f3\u306e\u958b\u767a<br>\u4ef2\u5730 \u3086\u305f\u304b<sup>1<\/sup>, \u675c \u5efa\u5f6c<sup>1,2<\/sup>, \u6e21\u908a \u7406\u7d17<sup>1,3<\/sup>, \u67f3\u7530 \u60a0\u592a\u6717<sup>1<\/sup>, \u6587\u6771 \u7f8e\u7d00<sup>1<\/sup>, \u5ca9\u672c \u548c\u4e5f<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>Dept. Mol. Brain Sci., Grad. Sch. Med. Sci., Kumamoto Univ.,<sup> 2<\/sup>Dept. Geriatric Psychiat, Mental Health Ctr., Jiangnan Univ., <sup>3<\/sup>Dept. Psychiat, Icahn Sch. Med. at Mount Sinai)<\/p>\n\n\n\n<p><strong>PO-151<\/strong> \/ OS-103<br>PLANT: Protein language model for predicting the antigenicity of Influenza viruses<br>PLANT: \u30a4\u30f3\u30d5\u30eb\u30a8\u30f3\u30b6\u30a6\u30a4\u30eb\u30b9\u306e\u6297\u539f\u6027\u3092\u4e88\u6e2c\u3059\u308b\u30bf\u30f3\u30d1\u30af\u8cea\u8a00\u8a9e\u30e2\u30c7\u30eb<br>\u4f0a\u6771 \u6f64\u5e73<sup>1<\/sup>, \u5ddd\u4e45\u4fdd \u4fee\u4f51<sup>1<\/sup>, \u6d77\u91ce \u535a\u4eae<sup>1<\/sup>, Strange Adam<sup>1<\/sup>, Lytras Spyros<sup>1<\/sup>, Lilley Alice<sup>2<\/sup>, Harvey Ruth<sup>2<\/sup>, Lewis Nicola<sup>2<\/sup>, \u4f50\u85e4 \u4f73<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u6771\u4eac\u5927\u5b66\u533b\u79d1\u5b66\u7814\u7a76\u6240, <sup>2<\/sup>The Francis Crick Institute)<\/p>\n\n\n\n<p><strong>PO-152<\/strong> \/ OS-304<br>DeepRES: Deep learning enables reaction-based comprehensive enzyme screening<br>DeepRES: \u53cd\u5fdc\u60c5\u5831\u306b\u57fa\u3065\u304f\u7db2\u7f85\u7684\u306a\u9175\u7d20\u30b9\u30af\u30ea\u30fc\u30cb\u30f3\u30b0\u306e\u305f\u3081\u306e\u6df1\u5c64\u5b66\u7fd2\u30e2\u30c7\u30eb, \u5ee3\u7530 \u4f73\u4eae<sup>1<\/sup>, \u5c71\u7530 \u62d3\u53f8<sup>1,2,3,4<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66, <sup>2<\/sup>\u682a\u5f0f\u4f1a\u793e\u30e1\u30bf\u30b8\u30a7\u30f3, <sup>3<\/sup>\u30e1\u30bf\u30b8\u30a7\u30f3\u30bb\u30e9\u30d4\u30e5\u30fc\u30c6\u30a3\u30af\u30b9\u682a\u5f0f\u4f1a\u793e, <sup>4<\/sup>\u682a\u5f0f\u4f1a\u793edigzyme)<\/p>\n\n\n\n<p><strong>PO-153<\/strong> \/ OS-203<br>RNA Inverse Folding Using a Grammar-Guided Generative Model with Tree-Based Representations<br>\u6587\u6cd5\u8a98\u5c0e\u3068\u6728\u69cb\u9020\u8868\u73fe\u3092\u7528\u3044\u305f\u751f\u6210\u30e2\u30c7\u30eb\u306b\u3088\u308bRNA\u9006\u6298\u308a\u7573\u307f<br>\u6e21\u9089 \u5065\u592a\u90ce<sup>1<\/sup>, \u79cb\u5c71 \u771f\u90a3\u6597<sup>2<\/sup>, \u698a\u539f \u5eb7\u6587<sup>2 <\/sup>&nbsp;(<sup>1<\/sup>\u6176\u61c9\u7fa9\u587e\u5927\u5b66\u5927\u5b66\u9662 \u7406\u5de5\u5b66\u7814\u7a76\u79d1, <sup>2<\/sup>\u5317\u91cc\u5927\u5b66 \u672a\u6765\u5de5\u5b66\u90e8)<\/p>\n\n\n\n<p><strong>PO-154<\/strong> \/ OS-602<br>Multimodal Gene\u2013Environment Modeling of Disease Onset Using a Context-Aware Genome Language Model<br>\u30b2\u30ce\u30e0\u8a00\u8a9e\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u75be\u60a3\u767a\u75c7\u306b\u304a\u3051\u308b\u500b\u5225\u907a\u4f1d\u30fb\u74b0\u5883\u76f8\u4e92\u4f5c\u7528\u306e\u30de\u30eb\u30c1\u30e2\u30fc\u30c0\u30eb\u89e3\u6790<br>\u6afb\u6728 \u5b9f<sup>1<\/sup>, \u6d45\u7530 \u68a8\u6e56<sup>1<\/sup>, \u938c\u7530 \u771f\u7531\u7f8e<sup>2<\/sup>, \u5965\u91ce \u606d\u53f2<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u4eac\u90fd\u5927\u5b66\u5927\u5b66\u9662\u533b\u5b66\u7814\u7a76\u79d1\u30d3\u30c3\u30b0\u30c7\u30fc\u30bf\u533b\u79d1\u5b66, <sup>2<\/sup>\u5317\u91cc\u5927\u5b66\u672a\u6765\u5de5\u5b66\u90e8)<\/p>\n\n\n\n<p><strong>PO-155<\/strong> \/ OS-504<br>SlopeSearch: an improved slope-based alignment-free method for genome<br>SlopeSearch: \u30b2\u30ce\u30e0\u985e\u4f3c\u6027\u691c\u7d22\u306e\u305f\u3081\u306e\u659c\u7387\u30d9\u30fc\u30b9\u975e\u30a2\u30e9\u30a4\u30e1\u30f3\u30c8\u6cd5\u306e\u6539\u826f\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0<br>CHEN YE<sup>1<\/sup>, Frith Martin<sup>1<\/sup> (<sup>1<\/sup>The University of Tokyo)<\/p>\n\n\n\n<p><strong>PO-156<br><\/strong>Information analysis of 25 types of biomarker proteins that predict the onset of dementia<br>\u8a8d\u77e5\u75c7\u306e\u767a\u75c7\u4e88\u6e2c\u3092\u62c5\u304625 \u7a2e\u985e\u306e\u30d0\u30a4\u30aa\u30de\u30fc\u30ab\u30fc\u30bf\u30f3\u30d1\u30af\u8cea\u7fa4\u306e\u60c5\u5831\u89e3\u6790<br>\u539f\u8302 \u6075\u7f8e\u5b50<sup>1,2<\/sup>, \u6797 \u5343\u5c0b<sup>1,2<\/sup>, \u548c\u8cc0 \u5dcc<sup>1,2,3 <\/sup>&nbsp;(<sup>1<\/sup>\u30d5\u30a9\u30fc\u30cd\u30b9\u30e9\u30a4\u30d5\u682a\u5f0f\u4f1a\u793e, <sup>2<\/sup>NEC\u30bd\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u30a4\u30ce\u30d9\u30fc\u30bf\u682a\u5f0f\u4f1a\u793e, <sup>3<\/sup>\u6771\u5317\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-157<br><\/strong>TGF\u03b2-induced transcriptional machinery assembled on Myc TAD drives osteosarcoma development<br>Myc TAD\u4e0a\u306b\u5f62\u6210\u3055\u308c\u308bTGF\u03b2\u8a98\u5c0e\u6027\u306e\u8ee2\u5199\u88c5\u7f6e\u306f\u3001\u9aa8\u8089\u816b\u767a\u75c7\u3092\u4fc3\u9032\u3059\u308b<br>\u4e0a\u91ce \u667a\u4e5f<sup>1<\/sup>, \u4f0a\u85e4 \u516c\u6210<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u9577\u5d0e\u5927\u5b66\u533b\u6b6f\u85ac\u5b66\u7dcf\u5408\u7814\u7a76\u79d1 \u5206\u5b50\u816b\u760d\u751f\u7269\u5b66)<\/p>\n\n\n\n<p><strong>PO-158<br><\/strong>Cell type\u2013specific functions of nucleic acid-binding proteins revealed by deep learning on co-expression networks<br>\u5404\u7d30\u80de\u7a2e\u306e\u6838\u9178\u7d50\u5408\u30bf\u30f3\u30d1\u30af\u8cea\u306e\u767a\u73fe\u5236\u5fa1\u907a\u4f1d\u5b50\u3068\u6a5f\u80fd\u3092\u4e88\u6e2c\u3059\u308b\u624b\u6cd5\u306e\u958b\u767a<br>\u5927\u91cc \u76f4\u6a39<sup>1<\/sup>, \u4f50\u85e4 \u5065\u543e<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-159<br><\/strong>System-Wide Alteration of Transcription Factor Regulatory Networks During Neural Differentiation in Down Syndrome<br>\u8ee2\u5199\u5236\u5fa1\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u7528\u3044\u305f\u30c0\u30a6\u30f3\u75c7\u5019\u7fa4\u306b\u304a\u3051\u308b\u795e\u7d4c\u5206\u5316\u80fd\u306e\u89e3\u6790<br>\u6c60\u5185 \u9999\u83dc\u5b50<sup>1,2<\/sup>, \u68ee\u5ca1 \u52dd\u6a39<sup>1<\/sup>, \u4fe1\u5b9a \u77e5\u6c5f<sup>1,2<\/sup>, \u7c95\u5ddd \u96c4\u4e5f<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u7406\u5316\u5b66\u7814\u7a76\u6240 \u751f\u547d\u533b\u79d1\u5b66\u7814\u7a76\u30bb\u30f3\u30bf\u30fc \u751f\u547d\u533b\u79d1\u5b66\u5927\u5bb9\u91cf\u30c7\u30fc\u30bf\u6280\u8853\u7814\u7a76\u30c1\u30fc\u30e0, <sup>2<\/sup>\u6a2a\u6d5c\u5e02\u7acb\u5927\u5b66\u5927\u5b66\u9662\u3000\u751f\u547d\u533b\u79d1\u5b66\u7814\u7a76\u79d1\u751f\u547d\u533b\u79d1\u5b66\u5c02\u653b)<\/p>\n\n\n\n<p><strong>PO-160<br><\/strong>Scarpia: A novel approach for detecting allele-specific copy number alteration in cancer genomes based on extended PELT algorithm<br>Scarpia: \u62e1\u5f35PELT\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u7528\u3044\u305f\uff0c\u304c\u3093\u7d44\u7e54\u306b\u304a\u3051\u308b\u30a2\u30ec\u30eb\u7279\u7570\u7684\u30b3\u30d4\u30fc\u6570\u5909\u5316\u3092\u540c\u5b9a\u3059\u308b\u65b0\u898f\u30a2\u30d7\u30ed\u30fc\u30c1\u306e\u958b\u767a<br>\u4f0a\u85e4 \u4f51<sup>1,2<\/sup>, \u9234\u6728 \u5275<sup>1<\/sup>, \u5742\u672c \u7965\u99ff<sup>1<\/sup>, \u767d\u77f3 \u53cb\u4e00<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u7814\u7a76\u6240\u3000\u30b2\u30ce\u30e0\u89e3\u6790\u57fa\u76e4\u958b\u767a\u5206\u91ce, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66\u533b\u5b66\u90e8\u9644\u5c5e\u75c5\u9662\u3000\u547c\u5438\u5668\u5185\u79d1)<\/p>\n\n\n\n<p><strong>PO-161<br><\/strong>BWT-based de novo interspersed repeat detection for large-scale genomes<br>BWT\u3092\u7528\u3044\u305f\u5de8\u5927\u30b2\u30ce\u30e0\u306e\u305f\u3081\u306e\u6563\u5728\u53cd\u5fa9\u914d\u5217\u691c\u51fa<br>\u6b66\u7530 \u6df3\u5fd7<sup>1<\/sup>, \u798f\u6c38 \u6d25\u5d69<sup>2,3<\/sup>, \u6d5c\u7530 \u9053\u662d<sup>3,4,5<\/sup>&nbsp; (<sup>1<\/sup>\u65e9\u7a32\u7530\u5927\u5b66\u5148\u9032\u7406\u5de5\u5b66\u7814\u7a76\u79d1, <sup>2<\/sup>\u6176\u61c9\u7fa9\u587e\u5927\u5b66\u7406\u5de5\u5b66\u90e8, <sup>3<\/sup>\u65e9\u7a32\u7530\u5927\u5b66\u7406\u5de5\u5b66\u8853\u9662, <sup>4<\/sup>\u7523\u696d\u6280\u8853\u7dcf\u5408\u7814\u7a76\u6240 \u7d30\u80de\u5206\u5b50\u5de5\u5b66\u7814\u7a76\u90e8\u9580, <sup>5<\/sup>\u65e5\u672c\u533b\u79d1\u5927\u5b66 \u533b\u5b66\u7814\u7a76\u79d1)<\/p>\n\n\n\n<p><strong>PO-162<\/strong> \/ OS-104<br>Estimating the impact of haplotype-phased SNVs on protein structure and transcriptional expression<br>\u30cf\u30d7\u30ed\u30bf\u30a4\u30d7\u306b\u95a2\u9023\u3059\u308bSNV\u306b\u3088\u308b\u30bf\u30f3\u30d1\u30af\u8cea\u7acb\u4f53\u69cb\u9020\u304a\u3088\u3073\u8ee2\u5199\u767a\u73fe\u3078\u306e\u5f71\u97ff\u63a8\u5b9a<br>\u5927\u5e73 \u6b63\u8cb4<sup>1,2<\/sup>, \u9577\ufa11 \u6b63\u6717<sup>3,4<\/sup>, \u571f\u539f \u4e00\u54c9<sup>1,2<\/sup>, \u5c71\u4e0b \u7406\u5b87<sup>1,5<\/sup>&nbsp; (<sup>1<\/sup>\u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc \u5148\u7aef\u533b\u7642\u958b\u767a\u30bb\u30f3\u30bf\u30fc \u30c8\u30e9\u30f3\u30b9\u30ec\u30fc\u30b7\u30e7\u30ca\u30eb\u30a4\u30f3\u30d5\u30a9\u30de\u30c6\u30a3\u30af\u30b9\u5206\u91ce, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662 \u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1 \u5148\u7aef\u751f\u547d\u79d1\u5b66\u5c02\u653b, <sup>3<\/sup>\u4e5d\u5dde\u5927\u5b66 \u751f\u4f53\u9632\u5fa1\u533b\u5b66\u7814\u7a76\u6240 \u9ad8\u6df1\u5ea6\u30aa\u30df\u30af\u30b9\u30b5\u30a4\u30a8\u30f3\u30b9\u30bb\u30f3\u30bf\u30fc \u30d0\u30a4\u30aa\u30e1\u30c7\u30a3\u30ab\u30eb\u60c5\u5831\u89e3\u6790\u5206\u91ce, <sup>4<\/sup>\u4eac\u90fd\u5927\u5b66\u5927\u5b66\u9662\u533b\u5b66\u7814\u7a76\u79d1\u9644\u5c5e\u30b2\u30ce\u30e0\u533b\u5b66\u30bb\u30f3\u30bf\u30fc, <sup>5<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662 \u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1 \u30e1\u30c7\u30a3\u30ab\u30eb\u60c5\u5831\u751f\u547d\u5c02\u653b)<\/p>\n\n\n\n<p><strong>PO-163<br><\/strong>The catalytic fold of PARG1 RhoGAP for RhoA protein promoted by the C1 domain and deduced mutational effects of NSCL\/P on the complex structure<br>C1\u30c9\u30e1\u30a4\u30f3\u306b\u3088\u3063\u3066\u4fc3\u9032\u3055\u308c\u308bPARG1 RhoGAP\u306eRhoA\u86cb\u767d\u8cea\u306e\u89e6\u5a92\u69cb\u9020\u3068\u57fa\u8cea\u8907\u5408\u4f53\u306b\u53ca\u307c\u3059\u5148\u5929\u6027\u53e3\u84cb\u88c2\u5909\u7570\u306e\u63a8\u5b9a\u3055\u308c\u308b\u5f71\u97ff<br>\u6cb3\u5185 \u5168<sup>1<\/sup>, \u5c0f\u5cf6 \u6b63\u6a39<sup>2<\/sup>, \u5742\u5143 \u4e00\u771f<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u611b\u77e5\u770c\u533b\u7642\u7642\u80b2\u7dcf\u5408\u30bb\u30f3\u30bf\u30fc \u767a\u9054\u969c\u5bb3\u7814\u7a76\u6240 \u795e\u7d4c\u60c5\u5831\u7814\u7a76\u90e8, <sup>2<\/sup>\u6771\u4eac\u85ac\u79d1\u5927\u5b66 \u751f\u547d\u79d1\u5b66\u90e8 \u751f\u7269\u60c5\u5831\u79d1\u5b66\u7814\u7a76\u5ba4)<\/p>\n\n\n\n<p><strong>PO-164<\/strong> \/ OS-402<br>Analysis and Prediction of the Impact of Anticancer Drugs on Gut Microbiota<br>\u304c\u3093\u6cbb\u7642\u85ac\u306b\u3088\u308b\u8178\u5185\u7d30\u83cc\u53e2\u306e\u5909\u5316\u89e3\u6790\u3068\u4e88\u6e2c\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9<br>\u6817\u539f \u606d\u5b50<sup>1,2<\/sup>, \u9152\u4e95 \u4fca\u8f14<sup>1,2<\/sup>, \u98ef\u7530 \u76f4\u5b50<sup>3<\/sup>, \u6fa4\u7530 \u61b2\u592a\u90ce<sup>4<\/sup>, \u6d1e\u6fa4 \u667a\u81f3<sup>3<\/sup>, \u85e4\u6fa4 \u5b5d\u592b<sup>3,5<\/sup>, \u4e2d\u6751 \u80fd\u7ae0<sup>3,6<\/sup>, \u5f71\u5c71 \u4fca\u4e00\u90ce<sup>2<\/sup>, \u571f\u539f \u4e00\u54c9<sup>1,2<\/sup>, \u5c71\u4e0b \u7406\u5b87<sup>2,7<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66\u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1\u5148\u7aef\u751f\u547d\u79d1\u5b66\u5c02\u653b, <sup>2<\/sup>\u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u5148\u7aef\u533b\u7642\u958b\u767a\u30bb\u30f3\u30bf\u30fc\u30c8\u30e9\u30f3\u30b9\u30ec\u30fc\u30b7\u30e7\u30ca\u30eb\u30a4\u30f3\u30d5\u30a9\u30de\u30c6\u30a3\u30af\u30b9\u5206\u91ce, <sup>3<\/sup>\u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u6771\u75c5\u9662\u533b\u85ac\u54c1\u958b\u767a\u63a8\u9032\u90e8\u30c8\u30e9\u30f3\u30b9\u30ec\u30fc\u30b7\u30e7\u30ca\u30eb\u30ea\u30b5\u30fc\u30c1\u652f\u63f4\u5ba4, <sup>4<\/sup>\u91e7\u8def\u52b4\u707d\u75c5\u9662\u816b\u760d\u5185\u79d1, <sup>5<\/sup>\u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u6771\u75c5\u9662\u982d\u9838\u90e8\u5185\u79d1, <sup>6<\/sup>\u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u6771\u75c5\u9662\u6d88\u5316\u7ba1\u5185\u79d1, <sup>7<\/sup>\u6771\u4eac\u5927\u5b66\u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1\u30e1\u30c7\u30a3\u30ab\u30eb\u60c5\u5831\u751f\u547d\u5c02\u653b)<\/p>\n\n\n\n<p><strong>PO-165<\/strong> \/ OS-502<br>Generative model for deciphering the relationship between mtDNA genotype and phenotypic heterogeneity<br>cloneVI:mtDNA\u591a\u69d8\u6027\u3068\u8868\u73fe\u578b\u4e0d\u5747\u4e00\u6027\u306e\u9023\u95a2\u3092\u8aad\u307f\u89e3\u304f\u305f\u3081\u306e\u6df1\u5c64\u751f\u6210\u30e2\u30c7\u30eb<br>\u65e5\u6bd4 \u592a\u667a<sup>1,2,3<\/sup>, \u5c0f\u5d8b \u6cf0\u5f18<sup>2<\/sup>, \u5cf6\u6751 \u5fb9\u5e73<sup>3<\/sup>&nbsp; (<sup>1<\/sup>\u540d\u53e4\u5c4b\u5927\u5b66\u5927\u5b66\u9662\u533b\u5b66\u7cfb\u7814\u7a76\u79d1\u7dcf\u5408\u533b\u5b66\u5c02\u653b\u30c7\u30fc\u30bf\u99c6\u52d5\u751f\u7269\u5b66\u5206\u91ce, <sup>2<\/sup>\u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u7814\u7a76\u6240\u8a08\u7b97\u751f\u547d\u79d1\u5b66\u30e6\u30cb\u30c3\u30c8, <sup>3<\/sup>\u6771\u4eac\u79d1\u5b66\u5927\u5b66\u96e3\u6cbb\u75be\u60a3\u7814\u7a76\u6240\u8a08\u7b97\u30b7\u30b9\u30c6\u30e0\u751f\u7269\u5b66\u5206\u91ce)<\/p>\n\n\n\n<p><strong>PO-166<\/strong> \/ OS-404<br>Unsupervised annotation of spatial transcriptomes based on vectorial information<br>\u30d9\u30af\u30c8\u30eb\u60c5\u5831\u306b\u57fa\u3065\u304f\u7a7a\u9593\u30c8\u30e9\u30f3\u30b9\u30af\u30ea\u30d7\u30c8\u30fc\u30e0\u306e\u6559\u5e2b\u306a\u3057\u30a2\u30ce\u30c6\u30fc\u30b7\u30e7\u30f3<br>\u91ce\u6751 \u4eae\u8f14<sup>1,2<\/sup>, \u9152\u4e95 \u4fca\u8f14<sup>1,3<\/sup>, \u5f71\u5c71 \u4fca\u4e00\u90ce<sup>1<\/sup>, \u5c71\u4e0b \u7406\u5b87<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>\u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u3000\u5148\u7aef\u533b\u7642\u958b\u767a\u30bb\u30f3\u30bf\u30fc\u3000\u30c8\u30e9\u30f3\u30b9\u30ec\u30fc\u30b7\u30e7\u30ca\u30eb\u30a4\u30f3\u30d5\u30a9\u30de\u30c6\u30a3\u30af\u30b9\u5206\u91ce, <sup>2<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662\u3000\u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1\u3000\u30e1\u30c7\u30a3\u30ab\u30eb\u60c5\u5831\u751f\u547d\u5c02\u653b, <sup>3<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662\u3000\u65b0\u9818\u57df\u5275\u6210\u79d1\u5b66\u7814\u7a76\u79d1\u3000\u5148\u7aef\u751f\u547d\u79d1\u5b66\u5c02\u653b)<\/p>\n\n\n\n<p><strong>PO-167<br><\/strong>ML-guided mass defect-based glycopeptide classifier for next-gen glycoproteomics<br>\u5f35 \u79c9\u5143<sup>2<\/sup>, Chau The Huong <sup>1<\/sup>, Kawahara Rebeca<sup>2<\/sup>, Matsui Yusuke<sup>2<\/sup>, Thaysen-Andersen Morten<sup>1,2<\/sup>&nbsp; (<sup>1<\/sup>Macquarie University, <sup>2<\/sup>Nagoya University)<\/p>\n\n\n\n<p><strong>PO-168<\/strong> \/ OS-503<br>Rewinding Time to Trace the Cooperative Evolution of Genes and Transcriptional Regulation<br>\u6642\u9593\u306e&#8221;\u5dfb\u304d\u623b\u3057&#8221;\u306b\u3088\u308b\u907a\u4f1d\u5b50\u3068\u8ee2\u5199\u5236\u5fa1\u306e\u5354\u8abf\u7684\u9032\u5316\u306e\u89e3\u660e<br>\u539f \u96c4\u4e00\u90ce<sup>1,2<\/sup>, \u5409\u6ca2 \u76f4\u5b50<sup>2<\/sup>, \u590f\u76ee \u8c4a\u5f70<sup>2<\/sup>, \u548c\u7530 \u6dbc\u5b50<sup>2<\/sup>, \u8c4a\u7530 \u6566<sup>3<\/sup>, \u5ddd\u8def \u82f1\u54c9<sup>2<\/sup>&nbsp; (<sup>1<\/sup>\u5317\u91cc\u5927\u5b66 \u672a\u6765\u5de5\u5b66\u90e8, <sup>2<\/sup>\u6771\u4eac\u90fd\u533b\u5b66\u7dcf\u5408\u7814\u7a76\u6240 \u30b2\u30ce\u30e0\u533b\u5b66\u7814\u7a76\u30bb\u30f3\u30bf\u30fc, <sup>3<\/sup>\u56fd\u7acb\u907a\u4f1d\u5b66\u7814\u7a76\u6240)<\/p>\n\n\n\n<p><strong>PO-169<br><\/strong>A Deep Learning Framework for Rapid and Scalable KEGG Orthology Annotation<br>\u6df1\u5c64\u5b66\u7fd2\u3092\u7528\u3044\u305f\u9ad8\u901f\u304b\u3064\u30b9\u30b1\u30fc\u30e9\u30d6\u30eb\u306aKEGG\u30aa\u30eb\u30bd\u30ed\u30b0\u6ce8\u91c8<br>YU ZHAOXI<sup>1<\/sup>, Meng Lingjie<sup>1<\/sup>, Nguyen Canh Hao<sup>1<\/sup>, \u99ac\u898b\u585a \u62d3<sup>1<\/sup>, \u91d1\u4e45 \u5be6<sup>1<\/sup>, \u7dd2\u65b9 \u535a\u4e4b<sup>1<\/sup>&nbsp; (<sup>1<\/sup>Kyoto University)<\/p>\n\n\n\n<p><strong>PO-170<\/strong> \/ OS-604<br>Decoding genomic and epigenomic diversity of transposable elements in brain<br>\u30d2\u30c8\u6b7b\u5f8c\u8133\u306b\u304a\u3051\u308b\u8ee2\u79fb\u56e0\u5b50\u306eGenetic\/Epigenetic\u306a\u591a\u69d8\u6027\u306e\u89e3\u6790<br>\u6e21\u908a \u7406\u7d17<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u30de\u30a6\u30f3\u30c8\u30b5\u30a4\u30ca\u30a4\u533b\u79d1\u5927\u5b66, <sup>2<\/sup>\u30ab\u30ea\u30d5\u30a9\u30eb\u30cb\u30a2\u5927\u5b66\u30ed\u30b5\u30f3\u30bc\u30eb\u30b9\u6821)<\/p>\n\n\n\n<p><strong>PO-171<br><\/strong>Molecular mechanisms of temperature-induced diapause and recovery in the hydrozoan Cladonema pacificum<br>\u30a8\u30c0\u30a2\u30b7\u30af\u30e9\u30b2\u306b\u304a\u3051\u308b\u6e29\u5ea6\u8a98\u5c0e\u6027\u306e\u4f11\u7720\u3068\u56de\u5fa9\u306e\u5206\u5b50\u30e1\u30ab\u30cb\u30ba\u30e0<br>\u5b87\u4e95 \u6df3\u4e00\u90ce<sup>1<\/sup>, \u9234\u6728 \u592a\u4e00<sup>2<\/sup>, \u5009\u6c38 \u82f1\u91cc\u5948<sup>2,3<\/sup>, \u4e09\u6d66 \u6b63\u5e78<sup>4<\/sup>, \u4e2d\u5d8b \u60a0\u4e00\u6717<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662\u85ac\u5b66\u7cfb\u7814\u7a76\u79d1, <sup>2<\/sup>\u6771\u5317\u5927\u5b66\u5927\u5b66\u9662\u751f\u547d\u79d1\u5b66\u7814\u7a76\u79d1, <sup>3<\/sup>\u4eac\u90fd\u5927\u5b66\u5927\u5b66\u9662\u85ac\u5b66\u7814\u7a76\u79d1, <sup>4<\/sup>\u57fa\u790e\u751f\u7269\u5b66\u7814\u7a76\u6240)<\/p>\n\n\n\n<p><strong>PO-172<\/strong> \/ OS-204<br>A Maximum Expected Accuracy\u2013Based RNA Secondary Structure Prediction Method Considering Loop Structures<br>\u30eb\u30fc\u30d7\u69cb\u9020\u3092\u8003\u616e\u3057\u305f\u6700\u5927\u671f\u5f85\u7cbe\u5ea6\u578bRNA\u4e8c\u6b21\u69cb\u9020\u4e88\u6e2c\u624b\u6cd5<br>\u592a\u7530\u57a3 \u5320<sup>1<\/sup>, \u6d45\u4e95 \u6f54<sup>1<\/sup>&nbsp; (<sup>1<\/sup>\u6771\u4eac\u5927\u5b66)<\/p>\n\n\n\n<p><strong>PO-173<br><\/strong>Predicting Functional Residues of Thymidine Kinases Using AlphaFold<br>AlphaFold\u3092\u7528\u3044\u305f\u30c1\u30df\u30b8\u30f3\u30ad\u30ca\u30fc\u30bc\u306e\u6a5f\u80fd\u6b8b\u57fa\u63a8\u5b9a<br>\u5c71\u53e3 \u9054\u751f<sup>1<\/sup>, \u674e \u79c0\u6804<sup>2<\/sup>, \u6e21\u90e8 \u5321\u53f2<sup>3<\/sup>, \u85e4\u5ba4 \u96c5\u5f18<sup>4<\/sup>, \u590f\u76ee \u3084\u3088\u3044<sup>1 <\/sup>&nbsp;(<sup>1<\/sup>\u533b\u85ac\u57fa\u76e4\u2022\u5065\u5eb7\u2022\u6804\u990a\u7814\u7a76\u6240\u3000AI\u5065\u5eb7\u2022\u533b\u85ac\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u3000\u30d0\u30a4\u30aa\u30a4\u30f3\u30d5\u30a9\u30de\u30c6\u30a3\u30af\u30b9\u30d7\u30ed\u30b8\u30a7\u30af\u30c8, <sup>2<\/sup>\u533b\u85ac\u57fa\u76e4\u2022\u5065\u5eb7\u2022\u6804\u990a\u7814\u7a76\u6240\u3000AI\u5065\u5eb7\u2022\u533b\u85ac\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u3000\u30a4\u30f3\u30b7\u30ea\u30b3\u30c7\u30b6\u30a4\u30f3\u30d7\u30ed\u30b8\u30a7\u30af\u30c8, <sup>3<\/sup>\u7409\u7403\u5927\u5b66\u5927\u5b66\u9662\u533b\u5b66\u7814\u7a76\u79d1\u3000\u30a6\u30a4\u30eb\u30b9\u5b66\u8b1b\u5ea7,<sup> 4<\/sup>\u4eac\u90fd\u85ac\u79d1\u5927\u5b66\u3000\u7d30\u80de\u751f\u7269\u5b66\u5206\u91ce)<\/p>\n\n\n\n<p><strong>PO-174<br><\/strong>Risk prediction of liver-related and cardiovascular events in steatotic liver disease: A machine learning analysis using a large-scale cohort<br>\u8102\u80aa\u6027\u809d\u75be\u60a3\u306b\u304a\u3051\u308b\u809d\u30fb\u5fc3\u8840\u7ba1\u30a4\u30d9\u30f3\u30c8\u306e\u30ea\u30b9\u30af\u4e88\u6e2c\uff1a\u5927\u898f\u6a21\u30b3\u30db\u30fc\u30c8\u3068\u6a5f\u68b0\u5b66\u7fd2\u3092\u7528\u3044\u305f\u89e3\u6790<br>\u82e5\u6797 \u4fca\u4e00<sup>1<\/sup>, \u6728\u6751 \u5cb3\u53f2<sup>1<\/sup>, \u7389\u57ce \u4fe1\u6cbb<sup>2<\/sup>, \u9ed2\u5d0e \u96c5\u4e4b<sup>2<\/sup>, \u7530\u4e2d \u76f4\u6a39<sup>3 <\/sup>(<sup>1<\/sup>\u4fe1\u5dde\u5927\u5b66\u533b\u5b66\u90e8\u5185\u79d1\u5b66\u7b2c\u4e8c\u6559\u5ba4\u30fb\u6d88\u5316\u5668\u5185\u79d1, <sup>2<\/sup>\u6b66\u8535\u91ce\u8d64\u5341\u5b57\u75c5\u9662\u6d88\u5316\u5668\u5185\u79d1, <sup>3<\/sup>\u4fe1\u5dde\u5927\u5b66\u533b\u5b66\u90e8\u56fd\u969b\u533b\u5b66\u7814\u7a76\u63a8\u9032\u5b66)<\/p>\n\n\n\n<p><strong>PO-175<br><\/strong>RatGene: Gene Deletion-Addition Algorithms Using Growth to Production Ratio for Growth-Coupled Production in Constraint-Based Metabolic Networks<br>RatGene: \u5236\u7d04\u306b\u57fa\u3065\u304f\u4ee3\u8b1d\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u304a\u3051\u308b\u6210\u9577-\u751f\u7523\u9023\u95a2\u306e\u305f\u3081\u306e\u6210\u9577-\u751f\u7523\u6bd4\u3092\u7528\u3044\u305f\u907a\u4f1d\u5b50\u524a\u9664\u3068\u8ffd\u52a0\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0<br>YIER MA<sup>1<\/sup>, TAMURA TAKEYUKI<sup>1<\/sup> (<sup>1<\/sup>Kyoto University)<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u8005\u3078\u306e\u3054\u6848\u5185 \u30dd\u30b9\u30bf\u30fc\u8cbc\u4ed8\u53ef\u80fd\u671f\u9593\uff1a\u30dd\u30b9\u30bf\u30fc\u306f9\u67083\u65e5\uff08\u6c34\uff099:30\u301c\u3001\u307e\u305f\u306f\u30019\u67084\u65e5\uff08\u6728\uff098:30\u301c\u306b\u6c7a\u3081\u3089\u308c\u305f\u30dd\u30b9\u30bf\u30fc\u756a\u53f7\u306e\u30dc\u30fc\u30c9\u306b\u63b2\u793a\u3057\u30019\u67085\u65e5\uff08\u6728\uff0913:00\u9803\u307e\u3067\u306b\u64a4\u53ce\u3092\u304a\u9858\u3044\u3044\u305f\u3057\u307e\u3059\u3002 \u30dd\u30b9 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"vkexunit_cta_each_option":"","footnotes":""},"class_list":["post-1628","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.jsbi.org\/iibmp2025\/wp-json\/wp\/v2\/pages\/1628","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.jsbi.org\/iibmp2025\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.jsbi.org\/iibmp2025\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.jsbi.org\/iibmp2025\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.jsbi.org\/iibmp2025\/wp-json\/wp\/v2\/comments?post=1628"}],"version-history":[{"count":27,"href":"https:\/\/www.jsbi.org\/iibmp2025\/wp-json\/wp\/v2\/pages\/1628\/revisions"}],"predecessor-version":[{"id":1821,"href":"https:\/\/www.jsbi.org\/iibmp2025\/wp-json\/wp\/v2\/pages\/1628\/revisions\/1821"}],"wp:attachment":[{"href":"https:\/\/www.jsbi.org\/iibmp2025\/wp-json\/wp\/v2\/media?parent=1628"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}