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01-12-2019 | Original Article | Issue 1/2019

Network Modeling Analysis in Health Informatics and Bioinformatics 1/2019

BicBioEC: biclustering in biomarker identification for ESCC

Journal:
Network Modeling Analysis in Health Informatics and Bioinformatics > Issue 1/2019
Authors:
P. Kakati, D. K. Bhattacharyya, J. K. Kalita
Important notes

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1007/​s13721-019-0200-x) contains supplementary material, which is available to authorized users.

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.

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Supplementary Material
Supplementary material 1 (pdf 100 KB)
13721_2019_200_MOESM1_ESM.pdf
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