Exploring alternative knowledge representations for protein secondary-structure prediction

Uros Midic, A. Keith Dunker, Zoran Obradovic

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Methods for 3-class secondary-structure prediction are thought to be reaching the highest achievable accuracy. Their accuracy on β-sheet residue class is considerably lower than for the other two classes. We analysed the relevance of 315 individual input attributes for a predictor with the usual framework of using sequence-profile based data with an input window of fixed size. We propose two alternative knowledge representations with significantly smaller sets of input attributes. We also investigated the possibility of exploiting the prediction of connected pairs of β-sheet residues and the prediction of residue contact maps for the improvement of accuracy of secondary-structure prediction.

Original languageEnglish (US)
Pages (from-to)286-313
Number of pages28
JournalInternational Journal of Data Mining and Bioinformatics
Volume1
Issue number3
DOIs
StatePublished - Jan 1 2007

Keywords

  • Bioinformatics
  • Data mining
  • Feature selection
  • Knowledge representation
  • Machine learning
  • Protein folding
  • Protein structure prediction
  • Sensitivity analysis

ASJC Scopus subject areas

  • Library and Information Sciences
  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)

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