Protein structural class prediction via k-separated bigrams using position specific scoring matrix

Harsh Saini, Gaurav Raicar, Alok Sharma, Sunil Lal, Abdollah Dehzangi, Rajeshkannan Ananthanarayanan, James Lyons, Neela Biswas, Kuldip K. Paliwal

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Protein structural class prediction (SCP) is as important task in identifying protein tertiary structure and protein functions. In this study, we propose a feature extraction technique to predict secondary structures. The technique utilizes bigram (of adjacent and k-separated amino acids) information derived from Position Specific Scoring Matrix (PSSM). The technique has shown promising results when evaluated on benchmarked Ding and Dubchak dataset.

Original languageEnglish (US)
Pages (from-to)474-479
Number of pages6
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume18
Issue number4
DOIs
StatePublished - Jul 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Bigram
  • K-separated bigram
  • PSSM
  • SCOP
  • SVM
  • Structural class prediction
  • Transition probabilities

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