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BicFinder: a biclustering algorithm for microarray data analysis

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Abstract

In the context of microarray data analysis, biclustering allows the simultaneous identification of a maximum group of genes that show highly correlated expression patterns through a maximum group of experimental conditions (samples). This paper introduces a heuristic algorithm called BicFinder (The BicFinder software is available at: http://www.info.univ-angers.fr/pub/hao/BicFinder.html) for extracting biclusters from microarray data. BicFinder relies on a new evaluation function called Average Correspondence Similarity Index (ACSI) to assess the coherence of a given bicluster and utilizes a directed acyclic graph to construct its biclusters. The performance of BicFinder is evaluated on synthetic and three DNA microarray datasets. We test the biological significance using a gene annotation web-tool to show that our proposed algorithm is able to produce biologically relevant biclusters. Experimental results show that BicFinder is able to identify coherent and overlapping biclusters.

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Authors and Affiliations

  1. UTIC, Higher School of Sciences and Technologies of Tunis, University of Tunis, 1008, Tunis, Tunisia

    Wassim Ayadi & Mourad Elloumi

  2. LERIA, University of Angers, 2 Boulevard Lavoisier, 49045, Angers, France

    Wassim Ayadi & Jin-Kao Hao

Authors
  1. Wassim Ayadi
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  2. Mourad Elloumi
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  3. Jin-Kao Hao
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Correspondence to Wassim Ayadi.

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Ayadi, W., Elloumi, M. & Hao, JK. BicFinder: a biclustering algorithm for microarray data analysis. Knowl Inf Syst 30, 341–358 (2012). https://doi.org/10.1007/s10115-011-0383-7

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  • DOI: https://doi.org/10.1007/s10115-011-0383-7

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