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PD_BiBIM: Biclustering-based biomarker identification in ESCC microarray data.

Pallabi Patowary¹, Dhruba K Bhattacharyya

¹Department of Computer Science and Engineering, Tezpur University, Tezpur, Assam, India.

Journal of Biosciences

|June 21, 2021

Summary

This summary is machine-generated.

This study identifies key biomarkers for esophageal squamous cell carcinoma (ESCC) using biclustering and network analysis. The findings aid in understanding disease progression and developing targeted therapies.

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Area of Science:

Bioinformatics
Genomics
Systems Biology

Background:

Identifying disease biomarkers is crucial for understanding disease progression.
Biclustering is an effective technique for identifying biologically significant biomarkers.
Existing biclustering methods often lack consistent performance across diverse datasets.

Purpose of the Study:

To evaluate and select robust biclustering techniques for biomarker discovery.
To identify significant biomarkers for esophageal squamous cell carcinoma (ESCC).
To construct and analyze biological networks for deeper biological insights.

Main Methods:

Evaluated multiple biclustering techniques based on enrichment significance.
Constructed biological networks from significant biclustering results.
Performed topological, pathway, and causal analysis on network modules.

Main Results:

Identified several potential biomarkers for ESCC, including IFNGR1, CLIC1, CDK4, and COPS5.
A ranking scheme was applied to prioritize identified biomarkers.
Network and pathway analyses provided insights into the biological roles of identified markers.

Conclusions:

The integrated approach of biclustering and network analysis is effective for biomarker discovery in ESCC.
The identified biomarkers warrant further investigation for their role in ESCC pathogenesis.
This study provides a framework for selecting robust biclustering methods for multi-disease biomarker identification.