Cluster structure inference for microarray data based on an information theoretic criterion

Author

Nicorici, Daniel ; Yli-Harja, Olli ; Astola, Jaakko

Author_Institution

Inst. of Signal Process., Tampere Univ. of Technol., Tampere

fYear

2006

fDate

28-30 May 2006

Firstpage

87

Lastpage

88

Abstract

This paper presents a new method for estimating the number of clusters and cluster structure in microarray data sets, based on minimum description length (MDL) principle and normalized maximum likelihood (NML) model for linear regression. The cluster-structure models for microarray data are compared based on the MDL principle. The performance of the new method is studied using simulated and real microarray data sets.

Keywords

biology computing; maximum likelihood estimation; pattern clustering; regression analysis; cluster structure inference; information theoretic criterion; linear regression; microarray data; minimum description length principle; normalized maximum likelihood model; Clustering algorithms; Data analysis; Electronic mail; Encoding; Gene expression; Linear regression; Maximum likelihood estimation; Signal processing; Signal processing algorithms; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Genomic Signal Processing and Statistics, 2006. GENSIPS '06. IEEE International Workshop on

Conference_Location

College Station, TX

Print_ISBN

1-4244-0384-7

Electronic_ISBN

1-4244-0385-5

Type

conf

DOI

10.1109/GENSIPS.2006.353169

Filename

4161790