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DeBic: A Differential Evolution Biclustering Algorithm for Microarray Data Analysis

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Artificial Intelligence: Theories and Applications (ICAITA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1769))

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Abstract

Biclustering is one of the interesting topics in bioinformatics and one of the crucial approaches to extracting meaningful information from data and performing high-dimensional analysis for gene expression data. However, since the colossal space complexity and the nature of the problem are proven to be NP-Hard, an approach to identifying valuable biclusters with a good quality measure is required in a reasonable amount of time. Moreover, metaheuristics and evolutionary computation algorithms have shown incredible success in this area. This paper offers a novel method of a Differential Evolution Based Biclustering algorithm to extract Biclusters called DeBic. The results of the experiments on the popular Yeast Cell-Cycle dataset indicate unique and interesting biclusters getting discovered with larger sizes.

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Authors and Affiliations

  1. University Mustapha STAMBOULI of Mascara, Mascara, Algeria

    Younes Charfaoui, Amina Houari & Fatma Boufera

Authors
  1. Younes Charfaoui
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  2. Amina Houari
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  3. Fatma Boufera
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Corresponding author

Correspondence to Younes Charfaoui .

Editor information

Editors and Affiliations

  1. University of Mascara, Mascara, Algeria

    Mohammed Salem

  2. University of Granada, Granada, Spain

    Juan Julián Merelo

  3. Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  4. University of Mascara, Mascara, Algeria

    Rochdi Bachir Bouiadjra

  5. University of Mascara, Mascara, Algeria

    Mohamed Debakla

  6. University of Mascara, Mascara, Algeria

    Fatima Debbat

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Charfaoui, Y., Houari, A., Boufera, F. (2023). DeBic: A Differential Evolution Biclustering Algorithm for Microarray Data Analysis. In: Salem, M., Merelo, J.J., Siarry, P., Bachir Bouiadjra, R., Debakla, M., Debbat, F. (eds) Artificial Intelligence: Theories and Applications. ICAITA 2022. Communications in Computer and Information Science, vol 1769. Springer, Cham. https://doi.org/10.1007/978-3-031-28540-0_23

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  • DOI: https://doi.org/10.1007/978-3-031-28540-0_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28539-4

  • Online ISBN: 978-3-031-28540-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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