Performance Improvement in Protein N-Myristoyl Classification by BONSAI with Insignificant Indexing Symbol

Manabu Sugii[1] (manabu@yamaguchi-u.ac.jp)
Ryo Okada[2] (r-okada@hcu.co.jp)
Hiroshi Matsuno[3] (matsuno@sci.yamaguchi-u.ac.jp)
Satoru Miyano[4] (miyano@ims.u-tokyo.ac.jp)

[1]Media and Information Technology Center, Organization for Academic Information, Yamaguchi University, 1677-1 Yoshida, Yamaguchi 753-8511, Japan
[2]Network Solution Group, Hitachi Chugoku Solutions, Ltd., 11-10 motomachi, Hiroshima 730-0011, Japan
[3]Graduate School of Science and Engineering, Yamaguchi University, 1677-1 Yoshida, Yamaguchi 753-8511, Japan
[4]Human Genome Center, University of Tokyo, Tokyo 108-8639,Japan.


Abstract

Many N-myristoylated proteins play key roles in regulating cellular structure and function. In the previous study, we have applied the machine learning system BONSAI to predict patterns based on which positive and negative examples could be classified. Although BONSAI has helped establish 2 interesting rules regarding the requirements for N-myristoylation, the accuracy rates of these rules are not satisfactory. This paper suggests an enhancement of BONSAI by introducing an “insignificant indexing symbol” and demonstrates the efficiency of this enhancement by showing an improvement in the accuracy rates. We further examine the performance of this enhanced BONSAI by comparing the results of classification obtained the proposed method and an existing public method for the same sets of positive and negative examples.

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Japanese Society for Bioinformatics