7 Mai 2018: Doctorat en Informatique, Université de Tunis El Manar, Faculté des Sciences de Tunis.
Septembre 2011-Juin 2013: Master en Informatique, Université de Mascara.
Septembre 2008-Juin 2011: Licence en Informatique, Université de Mascara.
Juin 2008 : Baccalauréat en mathématiques, Lycée Djamel Eddine El Afghani,Mascara.
La revue : Procedia Computer Science
Domaine : Informatique
Mots Clés : Biclustering; Negative correlations; Generic Association Rules ; Data mining; Bioinformatic; DNA microarray data.
Auteur : Amina Houari, Wassim Ayadi, Sadok Ben Yahia
Issn : 1877-0509 Eissn : vol : 112, Num : , pp : 278-287
Date de publication : 2017-11-10
Résume : A majority of existing biclustering algorithms for microarrays data focus only on extracting biclusters with positive correlations of genes. Nevertheless, biological studies show that a group of biologically significant genes may exhibit negative correlations. In this paper, we propose a new biclustering algorithm, called NBic-ARM. Based on Generic Association Rules, our algorithm identifies negatively-correlated genes. To assess NBic-ARM’s performance, we carried out exhaustive experiments on three real-life datasets.
Our results prove NBic-ARM’s ability to identify statistically and biologically significant biclusters.
La revue : International Journal of Machine Learning and Cybernetics
Domaine : Informatique
Mots Clés : Biclustering · Formal concept analysis · Data mining · Bioinformatics · DNA microarray data · Bond correlation measure
Auteur : Amina Houari, Wassim Ayadi, Sadok Ben Yahia
Issn : 1868-8071 Eissn : vol : 9, Num : 11, pp : 1879-1893
Date de publication : 2018-07-03
Résume : Biclustering has been very relevant within the field of gene expression data analysis. In fact, its main thrust stands in its ability to identify groups of genes that behave in the same way under a subset of samples (conditions). However, the pioneering algorithms of the literature has shown some limits in terms of the quality of unveiled biclusters. In this paper, we introduce a new algorithm, called BiFCA+, for biclustering microarray data. BiFCA+ heavily relies on the mathematical background of the formal concept analysis, in order to extract the set of biclusters. In addition, the Bond correlation measure is of use to filter out the overlapping biclusters. The extensive experiments, carried out on real-life datasets, shed light on BiFCA+’s ability to identify statistically and biologically significant biclusters.
