A novel hybrid GA/SVM system for protein sequences classification

Xing Ming Zhao*, De Shuang Huang, Yiu Ming CHEUNG, Hong Qiang Wang, Xin Huang

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingChapterpeer-review

21 Citations (Scopus)

Abstract

A novel hybrid genetic algorithm(GA)/Support Vector Machine (SVM) system, which selects features from the protein sequences and trains the SVM classifier simultaneously using a multi-objective genetic algorithm, is proposed in this paper. The system is then applied to classify protein sequences obtained from the Protein Information Resource (PIR) protein database. Finally, experimental results over six protein superfamilies are reported, where it is shown that the proposed hybrid GA/SVM system outperforms BLAST and HMMer.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsZheng Rong Yang, Richard Everson, Hujun Yin
PublisherSpringer Verlag
Pages11-16
Number of pages6
ISBN (Print)3540228810, 9783540228813
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3177
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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