Feature selection for genetic sequence classification. | Zendy

Nadia Chuzhanova | Zendy; Alan J. Jones | Zendy; Steve Margetts | Zendy

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Feature selection for genetic sequence classification.

Author(s) -

Nadia Chuzhanova,

Alan J. Jones,

Steve Margetts

Publication year - 1998

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/14.2.139

Subject(s) - pattern recognition (psychology) , feature (linguistics) , sequence (biology) , feature selection , feature vector , k nearest neighbors algorithm , artificial intelligence , selection (genetic algorithm) , phylogenetic tree , computer science , a priori and a posteriori , mathematics , data mining , biology , genetics , philosophy , linguistics , epistemology , gene

Most of the existing methods for genetic sequence classification are based on a computer search for homologies in nucleotide or amino acid sequences. The standard sequence alignment programs scale very poorly as the number of sequences increases or the degree of sequence identity is <30%. Some new computationally inexpensive methods based on nucleotide or amino acid compositional analysis have been proposed, but prediction results are still unsatisfactory and depend on the features chosen to represent the sequences.

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