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Article

A comparative study of machine-learning methods to predict the effects of single nucleotide polymorphisms on protein function.

Bioinformatics (Oxford, England) - 22 Nov 2003

Krishnan V G, Westhead D R

Abstract excerpt

MOTIVATION: The large volume of single nucleotide polymorphism data now available motivates the development of methods for distinguishing neutral changes from those which have real biological effects. Here, two different machine-learning methods, decision trees and support vector machines (SVMs), are applied for the first time to this problem. In common with most other methods, only non-synonymous changes in...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Identifiers

PMID: 14630648
DOI: 10.1093/bioinformatics/btg297

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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