Machine learning approaches to gene recognition

As laboratories round the world produce ever-greater volumes of DNA sequence data, efficient computational analysis techniques are becoming essential. This article surveys several efforts that apply machine learning techniques to gene recognition. Machine learning methods are well suited to sequence...

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Bibliographic Details
Published inIEEE expert Vol. 9; no. 2; pp. 2 - 10
Main Authors Craven, M.W., Shavlik, J.W.
Format Journal Article
LanguageEnglish
Published IEEE 01.04.1994
Subjects
Amino acids
Bioinformatics
Computer applications
Data analysis
DNA
Genetics
Genomics
Humans
Laboratories
Machine learning
Organisms
Sequences
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Summary:As laboratories round the world produce ever-greater volumes of DNA sequence data, efficient computational analysis techniques are becoming essential. This article surveys several efforts that apply machine learning techniques to gene recognition. Machine learning methods are well suited to sequence analysis because they can learn useful descriptions of genetic concepts when given only instances, rather than explicit definitions, of those concepts. This article looks at several such approaches to gene recognition in two broad classes: search by signal and search by content.< >
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ISSN:0885-9000
2374-9407
DOI:10.1109/64.294127