From cancer gene expression data to simple vital rules

Rattikorn Hewett, Ali Goksu, Soma Datta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Microarray gene expression profiling technology generates huge high-dimensional data. Finding analysis techniques that can cope with such data characteristics is crucial in Bioinformatics. This paper proposes a variation of an ensemble learning approach combined with a clustering technique to extract "simple" and yet "vital" rules from genomic data. The paper describes the approach and evaluates it on cancer gene expression data sets. We report experimental results including comparisons with other results obtained from a similar ensemble learning approach as well as some sophisticated techniques such as support vector machines.

Original languageEnglish
Title of host publication2006 IEEE Region 5 Conference
PublisherIEEE Computer Society
Pages329-334
Number of pages6
ISBN (Print)1424403596, 9781424403592
DOIs
StatePublished - Jan 1 2006
Event2006 IEEE Region 5 Conference - San Antonio, TX, United States
Duration: Apr 7 2006Apr 8 2006

Publication series

Name2006 IEEE Region 5 Conference

Conference

Conference2006 IEEE Region 5 Conference
CountryUnited States
CitySan Antonio, TX
Period04/7/0604/8/06

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  • Cite this

    Hewett, R., Goksu, A., & Datta, S. (2006). From cancer gene expression data to simple vital rules. In 2006 IEEE Region 5 Conference (pp. 329-334). [5507407] (2006 IEEE Region 5 Conference). IEEE Computer Society. https://doi.org/10.1109/TPSD.2006.5507407