Learning local languages and its application to protein α-chain identification

Takashi Yokomori, Nobuyuki Ishida, Satoshi Kobayashi

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

13 Citations (Scopus)

Abstract

This paper concerns an efficient algorithm for learning in the limit of a special type of regular languages called locally testable languages from positive data, and its application to identifying the protein α-chain region in amino acid sequences. First, we present a linear time algorithm that, given a locally testable language, learns (identifies) its deterministic finite state automaton in the limit from only positive data. This provides us with a practical and efficient learning method for a specific domain of symbolic analysis. We then describe several experimental results using the learning algorithm developed above. Following a theoretical observation which strongly suggests that a certain type of amino acid sequences can be expressed by a locally testable language, we apply the learning algorithm to identifying the protein α-chain region in amino acid sequences for hemoglobin. Experimental scores show an overall success rate of 95% correct identification for positive data, and 96% for negative data.

Original languageEnglish
Title of host publicationProceedings of the Hawaii International Conference on System Sciences
EditorsJay F. Nunamaker, Ralph H.Jr. Sprague
PublisherPubl by IEEE
Pages113-122
Number of pages10
ISBN (Print)0818650907
Publication statusPublished - 1995 Jan 1
Externally publishedYes
EventProceedings of the 27th Hawaii International Conference on System Sciences (HICSS-27). Part 4 (of 5) - Wailea, HI, USA
Duration: 1994 Jan 41994 Jan 7

Publication series

NameProceedings of the Hawaii International Conference on System Sciences
Volume5
ISSN (Print)1060-3425

Other

OtherProceedings of the 27th Hawaii International Conference on System Sciences (HICSS-27). Part 4 (of 5)
CityWailea, HI, USA
Period94/1/494/1/7

ASJC Scopus subject areas

  • Computer Science(all)

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