Literature DB >> 16013753

Grammatical inference in bioinformatics.

Yasubumi Sakakibara1.   

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.

Mesh:

Year:  2005        PMID: 16013753     DOI: 10.1109/TPAMI.2005.140

Source DB:  PubMed          Journal:  IEEE Trans Pattern Anal Mach Intell        ISSN: 0098-5589            Impact factor:   6.226


  8 in total

1.  Probabilistic grammatical model for helix-helix contact site classification.

Authors:  Witold Dyrka; Jean-Christophe Nebel; Malgorzata Kotulska
Journal:  Algorithms Mol Biol       Date:  2013-12-18       Impact factor: 1.405

2.  A composite method based on formal grammar and DNA structural features in detecting human polymerase II promoter region.

Authors:  Sutapa Datta; Subhasis Mukhopadhyay
Journal:  PLoS One       Date:  2013-02-20       Impact factor: 3.240

3.  A grammar inference approach for predicting kinase specific phosphorylation sites.

Authors:  Sutapa Datta; Subhasis Mukhopadhyay
Journal:  PLoS One       Date:  2015-04-17       Impact factor: 3.240

4.  Lineage grammars: describing, simulating and analyzing population dynamics.

Authors:  Adam Spiro; Luca Cardelli; Ehud Shapiro
Journal:  BMC Bioinformatics       Date:  2014-07-21       Impact factor: 3.169

5.  Use of a Novel Grammatical Inference Approach in Classification of Amyloidogenic Hexapeptides.

Authors:  Wojciech Wieczorek; Olgierd Unold
Journal:  Comput Math Methods Med       Date:  2016-03-09       Impact factor: 2.238

6.  Peptide vocabulary analysis reveals ultra-conservation and homonymity in protein sequences.

Authors:  Derek Gatherer
Journal:  Bioinform Biol Insights       Date:  2009-11-24

7.  A stochastic context free grammar based framework for analysis of protein sequences.

Authors:  Witold Dyrka; Jean-Christophe Nebel
Journal:  BMC Bioinformatics       Date:  2009-10-08       Impact factor: 3.169

8.  Developing JSequitur to Study the Hierarchical Structure of Biological Sequences in a Grammatical Inference Framework of String Compression Algorithms.

Authors:  Bulgan Galbadrakh; Kyung-Eun Lee; Hyun-Seok Park
Journal:  Genomics Inform       Date:  2012-12-31
  8 in total

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