Comparison of gene identification based on artificial neural network pre-processing with k-means cluster and principal component analysis

Leif E. Peterson; Matthew A Coleman

Comparison of gene identification based on artificial neural network pre-processing with k-means cluster and principal component analysis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A combination of gene ranking, dimensional reduction, and recursive feature elimination (RFE) using a BP-MLP artificial neural network (ANN) was used to select genes for DNA microarray classification. Use of k-means cluster analysis for dimensional reduction and maximum sensitivity for RFE resulted in 64-gene models with fewer invariant and correlated features when compared with PCA and mimimum error. In conclusion, k-means cluster analysis and sensitivity may be better suited for classifying diseases for which gene expression is more strongly influenced by pathway heterogeneity.

Original language	English (US)
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	267-276
Number of pages	10
Volume	3849 LNAI
State	Published - 2006
Externally published	Yes
Event	6th International Workshop - Fuzzy Logic and Applications - Crema, Italy Duration: Sep 15 2005 → Sep 17 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3849 LNAI
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	6th International Workshop - Fuzzy Logic and Applications
Country	Italy
City	Crema
Period	9/15/05 → 9/17/05

ASJC Scopus subject areas

Computer Science(all)
Biochemistry, Genetics and Molecular Biology(all)
Theoretical Computer Science

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Cite this

Peterson, L. E., & Coleman, M. A. (2006). Comparison of gene identification based on artificial neural network pre-processing with k-means cluster and principal component analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3849 LNAI, pp. 267-276). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3849 LNAI).

Comparison of gene identification based on artificial neural network pre-processing with k-means cluster and principal component analysis. / Peterson, Leif E.; Coleman, Matthew A.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3849 LNAI 2006. p. 267-276 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3849 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Peterson, LE & Coleman, MA 2006, Comparison of gene identification based on artificial neural network pre-processing with k-means cluster and principal component analysis. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3849 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3849 LNAI, pp. 267-276, 6th International Workshop - Fuzzy Logic and Applications, Crema, Italy, 9/15/05.

Peterson LE, Coleman MA. Comparison of gene identification based on artificial neural network pre-processing with k-means cluster and principal component analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3849 LNAI. 2006. p. 267-276. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Peterson, Leif E. ; Coleman, Matthew A. / Comparison of gene identification based on artificial neural network pre-processing with k-means cluster and principal component analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3849 LNAI 2006. pp. 267-276 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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