Learning conformation rules

Osamu Maruyama, Takayoshi Shoudai, Emiko Furuichi, Satoru Kuhara, Satoru Miyano

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Protein conformation problem, one of the hard and important problems, is to identify conformation rules which transform sequences to their tertiary structures, called conformations. Our aim of this work is to give a concrete theoretical foundation for graph-theoretic approach for the protein conformation problem in the framework of a probabilistic learning model. We propose the conformation problem as a learning problem from hypergraphs capturing the conformations of proteins in a loose way. Weconsider several classes of functions based on conformation rules, and show the PAC-learnability of them. The refutable PAC-learnability of functions is discussed, which would be helpful when a target function is not in the class of functions under consideration. We also report the conformation rules learned in our preliminary computational experiments.

本文言語英語
ホスト出版物のタイトルDiscovery Science - 4th International Conference, DS 2001, Proceedings
編集者Klaus P. Jantke, Ayumi Shinohara
出版社Springer Verlag
ページ243-257
ページ数15
ISBN(印刷版)9783540429562
DOI
出版ステータス出版済み - 2001
イベント4th International Conference on Discovery Science, DS 2001 - Washington, 米国
継続期間: 11 25 200111 28 2001

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2226
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他4th International Conference on Discovery Science, DS 2001
国/地域米国
CityWashington
Period11/25/0111/28/01

All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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