Analysis of Gene Expression Patterns Using Biclustering.
Mining microarray data to unearth interesting expression profile patterns for discovery of in silico biological knowledge is an emerging area of research in computational biology. A group of functionally related genes may have similar expression patterns under a set of conditions or at some time points. Biclustering is an important data mining tool that has been successfully used to analyze gene expression data for biologically significant cluster discovery. The purpose of this chapter is to introduce interesting patterns that may be observed in expression data and discuss the role of biclustering techniques in detecting interesting functional gene groups with similar expression patterns.
North-Eastern Hill University, Shillong, 793022, India. swarup@nehu.ac.in.
Swarup
S
Roy
Tezpur University, Napaam, India. dkb@tezu.ernet.in.
Dhruba K
DK
Bhattacharyya
University of Colorado, Colorado Spring, CO, USA. jkalita@uccs.edu.
Jugal K
JK
Kalita
D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D015203:Reproducibility of Results
Bi-clustering; Data mining; Expression patterns; Microarray