Functional classification of genes using semantic distance and fuzzy clustering approach: Evaluation with reference sets and overlap analysis

Abstract : Functional classification aims at grouping genes according to their molecular function or the biological process they participate in. Evaluating the validity of such unsupervised gene classification remains a challenge given the variety of distance measures and classification algorithms that can be used. We evaluate here functional classification of genes with the help of reference sets: KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathways and Pfam clans. These sets represent ground truth for any distance based on GO (Gene Ontology) biological process and molecular function annotations respectively. Overlaps between clusters and reference sets are estimated by the F-score method. We test our previously described IntelliGO semantic distance with hierarchical and fuzzy C-means clustering and we compare results with the state-of-the-art DAVID (Database for Annotation Visualisation and Integrated Discovery) functional classification method. Finally, study of best matching clusters to reference sets leads us to propose a set-difference method for discovering missing information.
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Article dans une revue
International journal of computational biology and drug design, Inderscience Enterprise, 2012, 5 (3/4), pp.245-260
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https://hal.inria.fr/hal-00734329
Contributeur : Malika Smail-Tabbone <>
Soumis le : vendredi 21 septembre 2012 - 14:32:16
Dernière modification le : jeudi 15 mars 2018 - 01:33:37

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  • HAL Id : hal-00734329, version 1

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Marie-Dominique Devignes, Benabderrahmane Sidahmed, Malika Smail-Tabbone, Amedeo Napoli, Poch Olivier. Functional classification of genes using semantic distance and fuzzy clustering approach: Evaluation with reference sets and overlap analysis. International journal of computational biology and drug design, Inderscience Enterprise, 2012, 5 (3/4), pp.245-260. 〈hal-00734329〉

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