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: Contribution to journalArticle

1 Citation (Scopus)

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
Pages (from-to)52-57
Number of pages6
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3177
Publication statusPublished - 2004 Dec 1
Externally publishedYes

Fingerprint

Proteins
Protein
Sequence Alignment
Sequence Homology
Genomics
Prediction
Functional Genomics
Predict
Feature Selection
Discrimination
Learning systems
Feature extraction
Pairwise
Machine Learning
Classifiers
Classifier
Subset
Relevance
Alternatives

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

An assessment of feature relevance in predicting protein function from sequence. / Al-Shahib, Ali; He, Chao; Tan, Aik-Choon; Girolami, Mark; Gilbert, David.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3177, 01.12.2004, p. 52-57.

Research output: Contribution to journalArticle

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