An assessment of feature relevance in predicting protein function from sequence

Ali Al-Shahib, Chao He, Aik Choon Tan, Mark Girolami, David Gilbert

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Improving the performance of protein function prediction is the ultimate goal for a bioinforraatician working in functional genomics. The classical prediction approach is to employ pairwise sequence alignments. However this method often faces difficulties when no statistically significant homologous sequences are identified. An alternative way is to predict protein function from sequence-derived features using machine learning. In this case the choice of possible features which can be derived from the sequence is of vital importance to ensure adequate discrimination to predict function. In this paper we have shown that carefully assessing the discriminative value of derived features by performing feature selection improves the performance of the prediction classifiers by eliminating irrelevant and redundant features. The subset selected from available features has also shown to be biologically meaningful as they correspond to features that have commonly been employed to assess biological function.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsZheng Rong Yang, Richard Everson, Hujun Yin
PublisherSpringer Verlag
Pages52-57
Number of pages6
ISBN (Print)3540228810, 9783540228813
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3177
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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