BicBioEC: biclustering in biomarker identification for ESCC

AbstractAnalysis of gene expression patterns enables identification of significant genes related to a specific disease. We analyze gene expression data for esophageal squamous cell carcinoma (ESCC) using biclustering, gene –gene network topology and pathways to identify significant biomarkers. Biclustering is a clustering technique by which we can extract coexpressed genes over a subset of samples. We introduce a parallel and robust biclustering algorithm to identify shifted, scaled and shifted-and-scaled biclusters of high biological relevance. Additionally, we introduce a mapping algorithm to establish the module–bicluster relationship across control and disease stages and a hub-gene identification method to support our analysis framework. The C-CUDA implementation of our biclustering algorithm makes the m ethod attractive due to faster speed and higher accuracy of results. Biomarkers such as CCNB1, CDK4, and KRT5 have been found to be closely associated with ESCC.
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - Category: Bioinformatics Source Type: research