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Erschienen in:
01.12.2019 | Original Article
BicBioEC: biclustering in biomarker identification for ESCC
Erschienen in: Network Modeling Analysis in Health Informatics and Bioinformatics | Ausgabe 1/2019
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Abstract
Analysis 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 method 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.