Graph based unsupervised feature selection for microarray data

Author

Swarnkar, T. ; MITRA, PINAKI

Author_Institution

Dept. of Comput. Sci. & Eng., Indian Institue of Technol., Kharagpur, India

fYear

2012

fDate

4-7 Oct. 2012

Firstpage

750

Lastpage

751

Abstract

Gene selection is an important step in microarray data analysis. In this paper we present an unsupervised feature selection technique for this purpose. The method constructs multiview graph based representation of samples using expression values gene sub clusters. Such individual views are further clustered using graph clustering techniques. We evaluate our technique using classification accuracies obtained from selecting representative genes from each such clusters.

Keywords

biology computing; data analysis; graph theory; pattern classification; pattern clustering; classification accuracy; expression values gene sub clusters; gene selection; graph based unsupervised feature selection; graph clustering techniques; microarray data analysis; multiview graph based representation; representative gene selection; Accuracy; Bioinformatics; Clustering algorithms; Conferences; Gene expression; Principal component analysis; Sensitivity; GUFS; Gene Selection; Hierarchical Clustering; Microarray; PCA; RELIEF; Unsupervised learning; Wrapper method; k-means;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedicine Workshops (BIBMW), 2012 IEEE International Conference on

Conference_Location

Philadelphia, PA

Print_ISBN

978-1-4673-2746-6

Electronic_ISBN

978-1-4673-2744-2

Type

conf

DOI

10.1109/BIBMW.2012.6470231

Filename

6470231