A Biclustering Method to Discover Co-regulated Genes Using Diverse Gene Expression Datasets

We propose a two-step biclustering approach to mine co-regulation patterns of a given reference gene to discover other genes that function in a common biological process. Currently, several successful methods utilize Pearson Correlation Coefficient (PCC) based gene expression analysis across all samples in datasets. However, microarray datasets are fraught with spurious samples or samples of diverse origin, and many genes/proteins that function in the same biological pathway may be missed. The novel PCC based biclustering algorithm introduced in this paper identifies subsets of genes with high correlation by stringently filtering the data and reducing false negatives due to spurious or unrelated samples in a dataset. Then, correlation information extracted from resulting biclusters are synthesized. We applied our method using the breast cancer associated tumor suppressors, BRCA1 and BRCA2, as the reference proteins to reveal genes and proteins important in the complex process of breast...

• Type: Journal
• Year: 2009
• Where: BICOB
• Authors: Doruk Bozdag, Jeffrey D. Parvin, Ümit V. Çatalyürek
• Keywords: Biclustering Approach, BICOB 2009, Computational Biology, Mitotic Spindle, Proteins