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BicGenesis: A Method to Identify ESCC Biomarkers Using the Biclustering Approach

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Proceedings of International Conference on Big Data, Machine Learning and Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 180))

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Abstract

Biclustering has already been established as an effective tool to study gene expression data toward interesting biomarker findings for a given disease. This paper examines the effectiveness of some prominent biclustering algorithms in extracting biclusters of high biological significance toward the identification of interesting biomarkers. We have chosen Esophageal Squamous Cell Carcinoma (ESCC) as a case for our empirical study and our method called BicGenesis could identify eight genes as possible biomarkers for ESCC.

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Author information

Authors and Affiliations

  1. Tezpur University, Napaam, Tezpur, Assam, India

    Manaswita Saikia & Dhruba K. Bhattacharyya

  2. College of Engineering and Applied Science, University of Colorado, Colorado Springs, CO, USA

    Jugal K. Kalita

Authors
  1. Manaswita Saikia
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  2. Dhruba K. Bhattacharyya
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  3. Jugal K. Kalita
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Corresponding author

Correspondence to Manaswita Saikia .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, National Institute Of Technology Silchar, Silchar, Cachar, Assam, India

    Ripon Patgiri

  2. CSE Department, National Institute of Technology Silchar, Silchar, Cachar, India

    Sivaji Bandyopadhyay

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

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Saikia, M., Bhattacharyya, D.K., Kalita, J.K. (2021). BicGenesis: A Method to Identify ESCC Biomarkers Using the Biclustering Approach. In: Patgiri, R., Bandyopadhyay, S., Balas, V.E. (eds) Proceedings of International Conference on Big Data, Machine Learning and Applications. Lecture Notes in Networks and Systems, vol 180. Springer, Singapore. https://doi.org/10.1007/978-981-33-4788-5_1

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