Learning conformation rules

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationDiscovery Science - 4th International Conference, DS 2001, Proceedings
EditorsKlaus P. Jantke, Ayumi Shinohara
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783540429562
Publication statusPublished - 2001
Event4th International Conference on Discovery Science, DS 2001 - Washington, United States
Duration: Nov 25 2001Nov 28 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other4th International Conference on Discovery Science, DS 2001
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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