A hybrid genetic algorithm approach for protein secondary structures

Yen Wei Chu*, Chuen-Tsai Sun

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

The importance of secondary protein structures is to help us to recognize many biological features, such as the structure and function of a protein, the evolutionary relation between proteins, and protein classification. Unfortunately, the secondary protein structures are hard to get from experimental analysis, and most researchers usually use predictive information instead of real structures. However, we want to look at the natures of protein secondary structures rather than putting too much focus on current approaches to protein secondary structure prediction. For the regularity of secondary protein structures, we define a schema and adopt an approach of evolutionary computation. The approach combines a clustering method and genetic algorithms to produce the schemata for the visible natures of protein secondary structures. There are two major roles of clustering algorithm, one is to generate part of the initial chromosomes in the genetic algorithms and the other is to assist schemata in predicting secondary protein structures. By performing the new approach, the accuracy of Q3 can be improved 12% more than the previous researches. Finally, we will demonstrate some schemata with interesting biological meaning.

Original languageEnglish
Title of host publicationProceedings of the World Congress on Intelligent Control and Automation (WCICA)
Pages3320-3324
Number of pages5
DOIs
StatePublished - 1 Dec 2006
Event6th World Congress on Intelligent Control and Automation, WCICA 2006 - Dalian, China
Duration: 21 Jun 200623 Jun 2006

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)
Volume1

Conference

Conference6th World Congress on Intelligent Control and Automation, WCICA 2006
CountryChina
CityDalian
Period21/06/0623/06/06

Keywords

  • Clustering
  • Data mining
  • Genetic algorithms
  • Knowledge discovery
  • Protein secondary structure

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