Fiche publication
Date publication
janvier 2012
Auteurs
Membres identifiés du Cancéropôle Est :
Dr DEVIGNES Marie-Dominique
,
Dr POCH Olivier
Tous les auteurs :
Devignes MD, Benabderrahmane S, Smail-Tabbone M, Napoli A, Poch O
Lien Pubmed
Résumé
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.
Référence
Int J Comput Biol Drug Des. 2012;5(3-4):245-60
