Gene expression data classification with revised kernel partial least S quares algorithm

Zhenqiu Liu, Dechang Chen

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

5 Citations (Scopus)

Abstract

One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel partial least squares (KPLS) and classification with logistic regression (discrimination) and other standard machine learning methods. KPLS is a generalization and nonlinear version of partial least squares (PLS). The proposed algorithm was applied to live different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.

Original languageEnglish (US)
Title of host publicationProceedings of the Seventeenth International FloridaArtificial Intelligence Research Society Conference, FLAIRS 2004
EditorsV. Barr, Z. Markov
Pages104-108
Number of pages5
Volume1
StatePublished - Dec 17 2004
EventProceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2004 - Miami Beach, FL, United States
Duration: May 17 2004May 19 2004

Other

OtherProceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2004
CountryUnited States
CityMiami Beach, FL
Period5/17/045/19/04

Fingerprint

Gene expression
Microarrays
Support vector machines
Learning systems
Logistics
Tumors
Statistical methods
Genes
Neural networks

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Liu, Z., & Chen, D. (2004). Gene expression data classification with revised kernel partial least S quares algorithm. In V. Barr, & Z. Markov (Eds.), Proceedings of the Seventeenth International FloridaArtificial Intelligence Research Society Conference, FLAIRS 2004 (Vol. 1, pp. 104-108)
Liu, Zhenqiu ; Chen, Dechang. / Gene expression data classification with revised kernel partial least S quares algorithm. Proceedings of the Seventeenth International FloridaArtificial Intelligence Research Society Conference, FLAIRS 2004. editor / V. Barr ; Z. Markov. Vol. 1 2004. pp. 104-108
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Liu, Z & Chen, D 2004, Gene expression data classification with revised kernel partial least S quares algorithm. in V Barr & Z Markov (eds), Proceedings of the Seventeenth International FloridaArtificial Intelligence Research Society Conference, FLAIRS 2004. vol. 1, pp. 104-108, Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2004, Miami Beach, FL, United States, 5/17/04.

Gene expression data classification with revised kernel partial least S quares algorithm. / Liu, Zhenqiu; Chen, Dechang.

Proceedings of the Seventeenth International FloridaArtificial Intelligence Research Society Conference, FLAIRS 2004. ed. / V. Barr; Z. Markov. Vol. 1 2004. p. 104-108.

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

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AB - One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel partial least squares (KPLS) and classification with logistic regression (discrimination) and other standard machine learning methods. KPLS is a generalization and nonlinear version of partial least squares (PLS). The proposed algorithm was applied to live different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.

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Liu Z, Chen D. Gene expression data classification with revised kernel partial least S quares algorithm. In Barr V, Markov Z, editors, Proceedings of the Seventeenth International FloridaArtificial Intelligence Research Society Conference, FLAIRS 2004. Vol. 1. 2004. p. 104-108