Learning local languages and their application to DNA sequence analysis

Takashi Yokomori, Satoshi Kobayashi

研究成果: Article

28 引用 (Scopus)

抜粋

This paper concerns an efficient algorithm for learning in the limit a special type of regular languages called strictly locally testable languages from positive data, and its application to identifying the protein a-chain region in amino acid sequences. First, we present a linear time algorithm that, given a strictly 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 method for learning a specific concept domain of sequence 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 a-chain region in amino acid sequences for hemoglobin. Experimental scores show an overall success rate of 95 percent correct identification for positive data, and 96 percent for negative data.

元の言語English
ページ(範囲)1067-1079
ページ数13
ジャーナルIEEE Transactions on Pattern Analysis and Machine Intelligence
20
発行部数10
DOI
出版物ステータスPublished - 1998 12 1

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

フィンガープリント Learning local languages and their application to DNA sequence analysis' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用