Bias and benefit induced by intra-species homologies in guilt by association methods to predict protein function

Laurent Brehelin 1 Olivier Gascuel 1, *
* Corresponding author
1 MAB - Méthodes et Algorithmes pour la Bioinformatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : The guilt by association (GBA) principle is used in several supervised and non-supervised methods to functionally annotate uncharacterized genes from transcriptomic data or from other information source. However, these methods do not distinguish between genes which have or have not intra-species homologues. We show here that functional annotation and intra-species homology are strongly dependent. We emphasize that applying any GBA method not accounting for this form of homology has two opposite effects: it leads to over-estimating the method performance on the genes with no intra-species homologues, and to losing the benefit of homology on the other genes. Bias and benefit are measured on \Pf\ and Yeast, and a general scheme to properly apply the GBA principle is given. All together, this method improves over previous standard applications of the GBA principle.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00113353
Contributor : Laurent Brehelin <>
Submitted on : Monday, November 13, 2006 - 10:39:44 AM
Last modification on : Thursday, October 4, 2018 - 10:58:05 AM
Long-term archiving on : Tuesday, April 6, 2010 - 10:21:05 PM

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Laurent Brehelin, Olivier Gascuel. Bias and benefit induced by intra-species homologies in guilt by association methods to predict protein function. JOBIM'06 : Journées ouvertes de Biologie, Informatique, Mathématiques, Jul 2006, pp.59-66. ⟨lirmm-00113353⟩

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