An Agent-Based Hybrid System for Microarray Data Analysis

Author

Zhang, Zili ; Yang, Pengyi ; Wu, Xindong ; Zhang, Chengqi

Author_Institution

Southwest Univ., Chongqing, China

Volume

24

Issue

5

fYear

2009

Firstpage

53

Lastpage

63

Abstract

This article reports our experience in agent-based hybrid construction for microarray data analysis. The contributions are twofold: We demonstrate that agent-based approaches are suitable for building hybrid systems in general, and that a genetic ensemble system is appropriate for microarray data analysis in particular. Created using an agent-based framework, this genetic ensemble system for microarray data analysis excels in both sample classification accuracy and gene selection reproducibility.

Keywords

biology computing; data analysis; genetic algorithms; genetics; learning (artificial intelligence); multi-agent systems; pattern classification; agent-based hybrid system; gene classification accuracy; gene selection reproducibility; genetic ensemble system; genetic-algorithm; microarray data analysis; multiagent system; Algorithm design and analysis; Australia; Bioinformatics; Data analysis; Genetic algorithms; Hybrid intelligent systems; Intelligent agent; Multiagent systems; Reproducibility of results; System testing; bioinformatics; data mining; hybrid systems; intelligent agents; microarray;

fLanguage

English

Journal_Title

Intelligent Systems, IEEE

Publisher

ieee

ISSN

1541-1672

Type

jour

DOI

10.1109/MIS.2009.92

Filename

5286172