Function prediction for DNA-/RNA-binding proteins, GPCRs, and drug ADME-associated proteins by SVM

Congzhong Cai*, Hanguang Xiao, Qianfei Yuan, Xinghua Liu, Yufeng Wen

*Corresponding author for this work

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

This paper explores the use of support vector machine (SVM) for protein function prediction. Studies are conducted on several groups of proteins with different functions including DNA-binding proteins, RNA-binding proteins, G-protein coupled receptors, drug absorption proteins, drug metabolizing enzymes, drug distribution and excretion proteins. The computed accuracy for the prediction of these proteins is found to be in the range of 82.32% to 99.7%, which illustrates the potential of SVM in facilitating protein function prediction.

Original languageEnglish
Pages (from-to)463-468
Number of pages6
JournalProtein and peptide letters
Volume15
Issue number5
DOIs
Publication statusPublished - Jun 2008
Externally publishedYes
Event3rd International Conference on Intelligent Computing - Qingdao, China
Duration: 21 Aug 200724 Aug 2007

Keywords

  • protein function prediction
  • DNA-binding proteins
  • RNA-binding proteins
  • G-protein coupled receptors (GPCRs)
  • drug absorption proteins
  • drug metabolizing enzymes
  • drug distribution and excretion proteins
  • support vector machine (SVM)
  • SUPPORT VECTOR MACHINES
  • SECONDARY STRUCTURE PREDICTION
  • AMINO-ACID-COMPOSITION
  • GENE-EXPRESSION
  • SEQUENCE
  • CLASSIFICATION
  • RECOGNITION
  • ALIGNMENT
  • NETWORKS
  • DATABASE

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