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Model and Data Engineering pp 139–153Cite as

Discovering Low Overlapping Biclusters in Gene Expression Data Through Generic Association Rules

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9344))

Abstract

Biclustering is a thriving and of paramount task in many biomedical applications. Indeed, the biclusters aim, among-others, the discovery of unveiling principles of cellular organizations and functions, to cite but a few.

In this paper, we introduce a new algorithm called, BiARM, that aims to efficiently extract the most meaningful, low overlapping biclusters. The main originality of our algorithm stands in the fact that it relies on the extraction of generic association rules. The reduced set of association rules faithfully mimics relationships between sets of genes, proteins, or other cell members and gives important information for the analysis of diseases. The effectiveness of our method has been proved through extensive carried out experiments on real-life DNA microarray data.

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Notes

  1. 1.

    Which may be either monotone increasing, monotone decreasing, up-down or down-up, etc.

  2. 2.

    Available at https://github.com/mehdi-kaytoue/trimax.

  3. 3.

    Available at http://arep.med.harvard.edu/biclustering/.

  4. 4.

    Available at http://arep.med.harvard.edu/biclustering/.

  5. 5.

    Available at http://llama.mshri.on.ca/funcassociate/.

  6. 6.

    The adjusted significance scores asses genes in each bicluster, which indicates how well they match with the different GO categories.

  7. 7.

    Available at http://db.yeastgenome.org/cgi-bin/GO/goTermFinder .

  8. 8.

    http://geneontology.org/.

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Author information

Authors and Affiliations

  1. LIPAH, Faculty of Siences of Tunis, University of Tunis El Manar, Tunis, Tunisia

    Amina Houari & Sadok Ben Yahia

  2. LATICE, Higher School of Siences and Technologies of Tunis, University of Tunis, Tunis, Tunisia

    Wassim Ayadi

  3. LERIA, University of Angres, Angers, France

    Wassim Ayadi

Authors
  1. Amina Houari
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  2. Wassim Ayadi
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  3. Sadok Ben Yahia
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Correspondence to Amina Houari .

Editor information

Editors and Affiliations

  1. National Engineering School for Mechanics and Aerotechnics, Poitiers, France

    Ladjel Bellatreche

  2. University of Thessaloniki, Thessaloniki, Greece

    Yannis Manolopoulos

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Houari, A., Ayadi, W., Yahia, S.B. (2015). Discovering Low Overlapping Biclusters in Gene Expression Data Through Generic Association Rules. In: Bellatreche, L., Manolopoulos, Y. (eds) Model and Data Engineering. Lecture Notes in Computer Science(), vol 9344. Springer, Cham. https://doi.org/10.1007/978-3-319-23781-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-23781-7_12

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