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Issue No.07 - July (2005 vol.27)
pp: 1051-1062
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.
INDEX TERMS
Index Terms- Grammatical inference, bioinformatics, molecular biology, hidden Markov model, stochastic context-free grammar.
CITATION
Yasubumi Sakakibara, "Grammatical Inference in Bioinformatics", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.27, no. 7, pp. 1051-1062, July 2005, doi:10.1109/TPAMI.2005.140
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