Grammatical inference in bioinformatics

Research output: Contribution to journalArticle

49 Citations (Scopus)

Abstract

Bioinformatics is an active research area aimed at developing intelligent systems for analyses of molecular biology. Many methods based on formal language theory, statistical theory, and learning theory have been developed for modeling and analyzing biological sequences such as DNA, RNA, and proteins. Especially, grammatical inference methods are expected to find some grammatical structures hidden in biological sequences. In this article, we give an overview of a series of our grammatical approaches to biological sequence analyses and related researches and focus on learning stochastic grammars from biological sequences and predicting their functions based on learned stochastic grammars.

Original languageEnglish
Pages (from-to)1051-1062
Number of pages12
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume27
Issue number7
DOIs
Publication statusPublished - 2005 Jul

Fingerprint

Grammatical Inference
Bioinformatics
Molecular biology
Formal languages
Intelligent systems
RNA
Grammar
DNA
Proteins
Learning Theory
Formal Languages
Molecular Biology
Intelligent Systems
Protein
Series
Modeling

Keywords

  • Bioinformatics
  • Grammatical inference
  • Hidden Markov model
  • Molecular biology
  • Stochastic context-free grammar

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

Cite this

Grammatical inference in bioinformatics. / Sakakibara, Yasubumi.

In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 7, 07.2005, p. 1051-1062.

Research output: Contribution to journalArticle

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