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Ons Maatouk

Informations générales

Ons Maatouk
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Maître Assistant

Publications

  • 2022
    Ons Maatouk, Emna Ayari, Hend Bouziri, Wassim Ayadi

    BOBEA: a bi-objective biclustering evolutionary algorithm for genome-wide association analysis.

    GECCO Companion 2022: 344-347, 2022

    Résumé

    The behavior of many diseases is still not well understood by researchers. Genome-Wide Association (GWA) analyzes have recently become a popular approach to discovering the genetic causes of many complex diseases. These analyzes could lead to the discovery of genetic factors potentially involved in certain disease susceptibility. These studies typically use the most common genetic variation between individuals, the Single Nucleotide Polymorphism (SNP). Indeed, many complex diseases have been revealed to be associated with combinations of SNP interactions. However, the identification of such interactions is considered difficult. Therefore, various unsupervised data mining methods are often developed in the literature to identify such variation involved in disease. In this work, a biclustering method is adopted to detect possible associations between SNP markers and disease susceptibility. It is an unsupervised classification technique, which plays an increasingly important role in the study of modern biology. We propose an evolutionary algorithm based on a bi-objective approach for the biclustering of the Genome-Wide Association. An experimental study is achieved on synthetic data to evaluate the performance of the proposed algorithm. Promising results are obtained.

  • Ons Maatouk, Wassim Ayadi, Hend Bouziri, Béatrice Duval

    Evolutionary Local Search Algorithm for the biclustering of gene expression data based on biological knowledge.

    Applied Soft Computing 104: 107177 (2021), 2021

    Résumé

    Biclustering is an unsupervised classification technique that plays an increasingly important role in the study of modern biology. This data mining technique has provided answers to several challenges raised by the analysis of biological data and more particularly the analysis of gene expression data. It aims to cluster simultaneously genes and conditions. These unsupervised techniques are based essentially on the assumption that the extraction of the co-expressed genes allows to have co-regulated genes. In addition, the integration of biological information in the search process may induce to the extraction of relevant and non-trivial biclusters. Therefore, this work proposes an evolutionary algorithm based on local search method that relies on biological knowledge. An experimental study is achieved on real microarray datasets to evaluate the performance of the proposed algorithm. The assessment and the comparison are based on statistical and biological criteria. A cross-validation experiment is also used to estimate its accuracy. Promising results are obtained. They demonstrate the importance of the integration of the biological knowledge in the biclustering process to foster the efficiency and to promote the discovery of non-trivial and biologically relevant biclusters.

  • Ons Maatouk, Wassim Ayadi, Hend Bouziri, Béatrice Duval

    Evolutionary biclustering algorithms: an experimental study on microarray data

    Soft Computing 23(17): 7671-7697, 2019

    Résumé

    The extraction of knowledge from large biological data is among the main challenges of bioinformatics. Several data mining techniques have been proposed to extract data; in this work, we focus on biclustering which has grown considerably in recent years. Biclustering aims to extract a set of genes with similar behavior under a condition set. In this paper, we propose an evolutionary biclustering algorithm and we analyze its performance by varying its genetic components. Hence, several versions of the evolutionary biclustering algorithm are introduced. Further, an experimental study is achieved on two real microarray datasets and the results are compared to other state-of-the-art biclustering algorithms. This thorough study allows to retain the best combination of operators among the various experienced choices.

  • Ons Maatouk, Wassim Ayadi, Hend Bouziri, Béatrice Duval

    Evolutionary Algorithm Based on New Crossover for the Biclustering of Gene Expression Data

    IAPR International Conference on Pattern Recognition in Bioinformatics, Pages 48-59, Springer, 2014

    Résumé

    Microarray represents a recent multidisciplinary technology. It measures the expression levels of several genes under different biological conditions, which allows to generate multiple data. These data can be analyzed through biclustering method to determinate groups of genes presenting a similar behavior under specific groups of conditions.
    This paper proposes a new evolutionary algorithm based on a new crossover method, dedicated to the biclustering of gene expression data. This proposed crossover method ensures the creation of new biclusters with better quality. To evaluate its performance, an experimental study was done on real microarray datasets. These experimentations show that our algorithm extracts high quality biclusters with highly correlated genes that are particularly involved in specific ontology structure.
  • Wassim Ayadi, Ons Maatouk, Hend Bouziri

    Evolutionary Biclustering Algorithm of Gene Expression Data

    DEXA Workshops 2012: 206-210, 2012

    Résumé

    Microarrays represent a new technology for measuring expression levels of several genes under various biological conditions generating multiple data. These data can be analyzed by using biclustering method which aims to extract a maximum number of genes and conditions presenting a similar behavior. This paper proposes a new evolutionary approach to obtain maximal high-quality biclusters of highly-correlated genes. The performance of the proposed algorithm is assessed on synthetic gene expression data. Experimental results show that our algorithm competes favorably with several state-of-the-art biclustering algorithms.