{"id":1718,"date":"2025-08-08T19:02:03","date_gmt":"2025-08-08T10:02:03","guid":{"rendered":"https:\/\/www.jsbi.org\/iibmp2025\/?page_id=1718"},"modified":"2025-08-08T19:02:04","modified_gmt":"2025-08-08T10:02:04","slug":"%e8%8b%a5%e6%89%8b%e3%82%bb%e3%83%ac%e3%82%af%e3%83%86%e3%82%a3%e3%83%83%e3%83%89%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\/%e8%8b%a5%e6%89%8b%e3%82%bb%e3%83%ac%e3%82%af%e3%83%86%e3%82%a3%e3%83%83%e3%83%89%e3%83%9d%e3%82%b9%e3%82%bf%e3%83%bc%e7%99%ba%e8%a1%a8\/","title":{"rendered":"\u82e5\u624b\u30bb\u30ec\u30af\u30c6\u30a3\u30c3\u30c9\u30dd\u30b9\u30bf\u30fc\u767a\u8868"},"content":{"rendered":"\n

\u82e5\u624b\u30bb\u30ec\u30af\u30c6\u30a3\u30c3\u30c9\u30dd\u30b9\u30bf\u30fc\u767a\u8868<\/h2>\n\n\n
\n\n\n\n\n
\u65e5\u6642\u30fb\u4f1a\u5834<\/strong><\/td>\n9\u67085\u65e5\uff08\u91d1\uff099\u664200\u5206\u301c10\u664230\u5206\u3000\u7b2c1\u4f1a\u5834<\/td>\n<\/tr>\n
\u5ea7\u9577<\/strong><\/td>\n\u9060\u91cc\u00a0\u7531\u4f73\u5b50 (\u7acb\u547d\u9928\u5927\u5b66)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n\n\n

PO-024\u3000\u6cb3\u91ce \u771f\u4e5f
<\/strong>Benchmarking Deep Learning Approaches for Predicting Protein-Ligand Interactions to Identify Therapeutic Inhibitors of AKR1B10
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
\u6cb3\u91ce \u771f\u4e5f1,2<\/sup>, \u4e94\u5341\u91cc \u5f701<\/sup>, \u9060\u85e4 \u667a\u53f23,4<\/sup>, \u5bcc\u4e95 \u5065\u592a\u90ce2<\/sup>\u00a0 (1<\/sup>\u5c90\u961c\u85ac\u79d1\u5927\u5b66\u30fb\u751f\u5316\u5b66, 2<\/sup>\u7523\u696d\u6280\u8853\u7dcf\u5408\u7814\u7a76\u6240 (AIST)\u30fb\u4eba\u5de5\u77e5\u80fd\u7814\u7a76\u30bb\u30f3\u30bf\u30fc, 3<\/sup>\u5c90\u961c\u5927\u5b66\u5927\u5b66\u9662\u9023\u5408\u5275\u85ac\u533b\u7642\u60c5\u5831\u7814\u7a76\u79d1, 4<\/sup>\u5c90\u961c\u5927\u5b66\u30fbCOMIT)<\/p>\n\n\n\n

PO-120\u3000\u4e0a\u7530 \u307f\u306e\u308a
<\/strong>Prediction of Kampo medicines to reduce side effects caused by Western medicines
\u897f\u6d0b\u85ac\u306b\u3088\u308b\u526f\u4f5c\u7528\u3092\u8efd\u6e1b\u3059\u308b\u6f22\u65b9\u85ac\u306e\u4e88\u6e2c
\u4e0a\u7530 \u307f\u306e\u308a1<\/sup>, \u4e00\u30ce\u702c \u97f3\u84491<\/sup>, \u4e80\u6df5 \u7531\u4e431<\/sup>, \u5cf6\u7530 \u7950\u6a391<\/sup>, \u6fa4\u7530 \u9686\u4ecb2<\/sup>, \u9580\u8107 \u771f3<\/sup>, \u5c71\u897f \u82b3\u88d54<\/sup>, \u5ca9\u7530 \u901a\u592b1<\/sup>\u00a0 (1<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66, 2<\/sup>\u5ca1\u5c71\u5927\u5b66, 3<\/sup>\u5bcc\u5c71\u5927\u5b66, 4<\/sup>\u540d\u53e4\u5c4b\u5927\u5b66)<\/p>\n\n\n\n

PO-006\u3000\u77f3\u4e95 \u6b69\u4f73
<\/strong>Deep-Learning-Based Generation of Protein-Binding RNA Sequences
\u6df1\u5c64\u5b66\u7fd2\u3092\u7528\u3044\u305f\u30bf\u30f3\u30d1\u30af\u8cea\u306b\u7d50\u5408\u3059\u308bRNA\u914d\u5217\u306e\u8a2d\u8a08
\u77f3\u4e95 \u6b69\u4f731<\/sup>, \u79cb\u5c71 \u771f\u90a3\u65972<\/sup>, \u698a\u539f \u5eb7\u65872<\/sup>\u00a0 (1<\/sup>\u6176\u61c9\u7fa9\u587e\u5927\u5b66, 2<\/sup>\u5317\u91cc\u5927\u5b66)<\/p>\n\n\n\n

PO-064\u3000\u9053\u4e0b \u6ec9\u4eba
<\/strong>Comprehensive Analysis of mRNA Fate Determination Regulated by Transcription and Translation Initiation Sites Shifts in Plants
\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
\u9053\u4e0b \u6ec9\u4eba1<\/sup>, \u6817\u5c71 \u670b\u5b502<\/sup>, \u6cb3\u5185 \u6b63\u6cbb1,2<\/sup>, \u677e\u4e95 \u53572<\/sup>, \u8494\u7530 \u7531\u5e03\u5b501,2<\/sup>, \u6817\u539f \u5fd7\u592b2<\/sup>\u00a0 (1<\/sup>\u524d\u6a4b\u5de5\u79d1\u5927\u5b66, 2<\/sup>\u7406\u7814CSRS)<\/p>\n\n\n\n

PO-066\u3000\u5b89\u7530 \u5343\u4e03
<\/strong>Comprehensive Analysis of Characteristics of Multi-Nucleotide Variants (MNV) in the Japanese Population
\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
\u5b89\u7530 \u5343\u4e031,2<\/sup>, \u5c3e\u5d0e \u907c1<\/sup>\u00a0 (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, 2<\/sup>\u7b51\u6ce2\u5927\u5b66\u751f\u547d\u74b0\u5883\u5b66\u7fa4\u751f\u7269\u5b66\u985e)<\/p>\n\n\n\n

PO-111\u3000\u9152\u4e95 \u4fca\u8f14
<\/strong>SpatialKNifeY (SKNY): Detection of the Tumor Microenvironment and Estimation of Tumor Progression Trajectory Using Spatial Transcriptomics of Breast Cancer 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
\u9152\u4e95 \u4fca\u8f141,2<\/sup>, \u91ce\u6751 \u4eae\u8f141,3<\/sup>, \u6c60 \u6210\u57fa1<\/sup>, \u6c38\u6fa4 \u61673,4<\/sup>, \u9234\u6728 \u7d62\u5b503<\/sup>, \u9234\u6728 \u7a633<\/sup>, \u77f3\u5ddd \u4fca\u5e735<\/sup>, \u571f\u539f \u4e00\u54c91,2<\/sup>, \u5f71\u5c71 \u4fca\u4e00\u90ce1<\/sup>, \u5c71\u4e0b \u7406\u5b871<\/sup>\u00a0 (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, 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, 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, 4<\/sup>\u56fd\u7acb\u304c\u3093\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u6771\u75c5\u9662 \u4e73\u817a\u5916\u79d1, 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

PO-108\u3000\u76f8\u5ddd \u54f2\u54c9
<\/strong>Segmentation method of approximate cell region using a quadtree in spatial transcriptomics
\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
\u76f8\u5ddd \u54f2\u54c91<\/sup>, \u6749\u5c71 \u97ff1<\/sup>, \u7e41\u7530 \u6d69\u529f1<\/sup>, \u6885\u8c37 \u4fca\u6cbb1<\/sup>, \u702c\u5c3e \u8302\u4eba1<\/sup>\u00a0 (1<\/sup>\u5927\u962a\u5927\u5b66\u5927\u5b66\u9662\u60c5\u5831\u79d1\u5b66\u7814\u7a76\u79d1)<\/p>\n\n\n\n

PO-143\u3000\u795e\u7530 \u88d5\u4e5f
<\/strong>Chaotic Search-Enhanced Real-Coded Genetic Algorithm
\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
\u795e\u7530 \u88d5\u4e5f1<\/sup>, \u9060\u91cc \u7531\u4f73\u5b501<\/sup>\u00a0 (1<\/sup>\u7acb\u547d\u9928\u5927\u5b66)<\/p>\n\n\n\n

PO-035\u3000\u51fa\u7c60 \u8429\u4eba
<\/strong>Deep Ensemble Learning for Predicting Treatment Outcomes in Heart Failure
\u6df1\u5c64\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u5b66\u7fd2\u306b\u3088\u308b\u5fc3\u4e0d\u5168\u306e\u6cbb\u7642\u4e88\u5f8c\u306e\u4e88\u6e2c
\u51fa\u7c60 \u8429\u4eba1<\/sup>, \u5019 \u8061\u5fd72<\/sup>, \u6234 \u54f2\u76932<\/sup>, \u85e4\u7530 \u5bdb\u59482<\/sup>, \u5c3e\u4e0a \u5065\u51503<\/sup>, \u91ce\u6751 \u5f81\u592a\u90ce2<\/sup>, \u5c0f\u5ba4 \u4e00\u62104<\/sup>, \u6ff1\u91ce \u6843\u5b501<\/sup>\u00a0 (1<\/sup>\u4e5d\u5dde\u5de5\u696d\u5927\u5b66, 2<\/sup>\u6771\u4eac\u5927\u5b66\u533b\u5b66\u90e8\u9644\u5c5e\u75c5\u9662, 3<\/sup>\u5948\u826f\u770c\u7acb\u533b\u79d1\u5927\u5b66\u9644\u5c5e\u75c5\u9662, 4<\/sup>\u56fd\u969b\u533b\u7642\u798f\u7949\u5927\u5b66)<\/p>\n\n\n\n

PO-015\u3000\u6751\u4e0a \u822a\u6c70
<\/strong>Evaluation and Applicability of Large-scale Vision-Language Models in the Japanese National Examination for Clinical Laboratory Technicians
\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, \u6751\u4e0a \u822a\u6c701<\/sup>, \u5c3e\u5d0e \u907c1,2<\/sup>, \u677e\u6fa4 \u4eae\u8f141<\/sup>, \u7530\u539f-\u65b0\u4e95 \u60a0\u4e5f1<\/sup>\u00a0 (1<\/sup>\u7b51\u6ce2\u5927\u5b66, 2<\/sup>\u7406\u5316\u5b66\u7814\u7a76\u6240)<\/p>\n","protected":false},"excerpt":{"rendered":"

\u82e5\u624b\u30bb\u30ec\u30af\u30c6\u30a3\u30c3\u30c9\u30dd\u30b9\u30bf\u30fc\u767a\u8868 \u65e5\u6642\u30fb\u4f1a\u5834 9\u67085\u65e5\uff08\u91d1\uff099\u664200\u5206\u301c10\u664230\u5206\u3000\u7b2c1\u4f1a\u5834 \u5ea7\u9577 \u9060\u91cc\u00a0\u7531\u4f73\u5b50 (\u7acb\u547d\u9928\u5927\u5b66) PO-024\u3000\u6cb3\u91ce \u771f\u4e5fBenchmarking Deep Learning Approa […]<\/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-1718","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.jsbi.org\/iibmp2025\/wp-json\/wp\/v2\/pages\/1718","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=1718"}],"version-history":[{"count":1,"href":"https:\/\/www.jsbi.org\/iibmp2025\/wp-json\/wp\/v2\/pages\/1718\/revisions"}],"predecessor-version":[{"id":1719,"href":"https:\/\/www.jsbi.org\/iibmp2025\/wp-json\/wp\/v2\/pages\/1718\/revisions\/1719"}],"wp:attachment":[{"href":"https:\/\/www.jsbi.org\/iibmp2025\/wp-json\/wp\/v2\/media?parent=1718"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}