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An Assessment of Feature Relevance in Predicting Protein Function from Sequence

  • Ali Al-Shahib
  • Chao He
  • Aik Choon Tan
  • Mark Girolami
  • David Gilbert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3177)

Abstract

Improving the performance of protein function prediction is the ultimate goal for a bioinformatician 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.

Keywords

Feature Selection Feature Subset Redundant Feature Protein Function Prediction Wrap Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Ali Al-Shahib
    • 1
  • Chao He
    • 1
  • Aik Choon Tan
    • 1
  • Mark Girolami
    • 1
  • David Gilbert
    • 1
  1. 1.Bioinformatics Research Centre, Department of Computing ScienceUniversity of GlasgowGlasgowUK

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