Differential biclustering for gene expression analysis

Authors:
Omar Odibat;Chandan K. Reddy;Craig N. Giroux
Affiliations:
Wayne State University, Detroit, MI;Wayne State University, Detroit, MI;Wayne State University, Detroit, MI
Venue:
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Year:
2010

Citing 5
Cited 2

Biclustering of Expression Data

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Biclustering Algorithms for Biological Data Analysis: A Survey

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Defining transcription modules using large-scale gene expression data

Bioinformatics
A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data

Bioinformatics
Biclustering in data mining

Computers and Operations Research

Constrained logistic regression for discriminative pattern mining

ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Ranking differential genes in co-expression networks

Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Biclustering algorithms have been successfully used to find subsets of co-expressed genes under subsets of conditions. In some cases, microarray experiments are performed to compare the biological activities of the genes between two classes of cells, such as normal and cancer cells. In this paper, we propose DiBiCLUS, a novel Differential Biclustering algorithm, to identify differential biclusters from the gene expression data where the samples belong to one of the two classes. The genes in these differential biclusters can be positively or negatively co-expressed. We introduce two criteria for any pair of genes to be considered as a differential pair across the two classes. To illustrate the performance of the proposed algorithm, we present the experimental results of applying DiBiCLUS algorithm on synthetic and reallife datasets. These experiments show that the identified differential biclusters are both statistically and biologically significant.