Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining.

ABSTRACT: Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discr

SUBMITTER: Maulik U 

PROVIDER: S-EPMC4382191 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature