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

Authors: Marie-Dominique Devignes; Sidahmed Benabderrahmane; Malika Smaïl-Tabbone; Amedeo Napoli; Olivier Poch

Addresses: LORIA (CNRS UMR7503, Lorraine University, INRIA), Equipe Orpailleur, Bâtiment B, Campus Scientifique, BP239, F-54506 Vandoeuvre les Nancy cedex, France. ' LORIA (CNRS UMR7503, Lorraine University, INRIA), Equipe Orpailleur, Bâtiment B, Campus Scientifique, BP239, F-54506 Vandoeuvre les Nancy cedex, France. ' LORIA (CNRS UMR7503, Lorraine University, INRIA), Equipe Orpailleur, Bâtiment B, Campus Scientifique, BP239, F-54506 Vandoeuvre les Nancy cedex, France. ' LORIA (CNRS UMR7503, Lorraine University, INRIA), Equipe Orpailleur, Bâtiment B, Campus Scientifique, BP239, F-54506 Vandoeuvre les Nancy cedex, France. ' IGBMC (CNRS UMR7104, Strasbourg University), Laboratory of Bioinformatics and Integrative Genomics, 1 rue Laurent Fries, F-67404 Illkirch Strasbourg, France

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.

Keywords: semantic similarity measures; gene ontology; gene functional classification; hierarchical clustering; fuzzy clustering; overlap analysis; gene functions; gene classification; semantic distance; reference sets.

DOI: 10.1504/IJCBDD.2012.049207

International Journal of Computational Biology and Drug Design, 2012 Vol.5 No.3/4, pp.245 - 260

Published online: 05 Dec 2014 *

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