A comparison and evaluation of five biclustering algorithms by quantifying goodness of biclusters for gene expression data.

ABSTRACT:

Background

Several biclustering algorithms have been proposed to identify biclusters, in which genes share similar expression patterns across a number of conditions. However, different algorithms would yield different biclusters and further lead to distinct conclusions. Therefore, some testing and comparisons between these algorithms are strongly required.

Methods

In this study, five biclustering algorithms (i.e. BIMAX, FABIA, ISA, QUBIC and SAMBA) were compared with each other in the cases where they were used to handle two expression datasets (GDS1620 and pathway) with different dimensions in Arabidopsis thaliana (A. thaliana)GO (gene ontology) annotation and PPI (protein-protein interaction) network were used to verify the corresponding biological significance of bicluste

SUBMITTER: Li L 

PROVIDER: S-EPMC3447720 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature