Recherche de classes empiétantes dans un graphe : application aux réseaux d’interactions entre protéines

Cet article présente une méthode de classification empiétante permettant de mettre en évidence des zones denses en arêtes dans un graphe. On cherche plus précisément à extraire du graphe des sous-graphes dont la densité en arêtes soit élevée par rapport à la densité du graphe entier, ces sous-graphes pouvant avoir des sommets en commun. Cette méthode est appliquée à un problème issu de la biologique : l’annotation des protéines. Les graphes considérés traduisent alors des interactions observées entre les protéines. Partant du principe biologique que des protéines impliquées dans une même fonction cellulaire interagissent, les sous-graphes obtenus par l’application de la méthode de classification empiétante aux réseaux d’interactions donnent des indications sur les fonctions des protéines constituant ces sous-graphes, ce qui permet de fournir une aide informatique à la prédiction de fonctions inconnues de certaines protéines. Le caractère empitétant autorisé par la méthode présentée ici permet en particulier de prendre en compte le fait que les protéines peuvent être impliquées chacune dans plusieurs fonctions cellulaires.This article describes a method of overlapping classification, in order to compute zones which are dense in edges in a graph. More precisely, the aim is to compute subgraphs in which the density of edges is large compared to the edge-density of the whole graph. These subgraphs may share common vertices. This method is applied to a problem arising in biology: the annotation of proteins. The graphs then represent the observed interactions between proteins. Thanks to the biological principle that proteins involved in the same cellular function interact, the subgraphs provided when the method is applied to the protein-protein interactions networks provide information about the functions of proteins belonging to these subgraphs. This provides a computer-aided tool for the prediction of unknown functions of some proteins. The overlapping allowed by the method depicted here makes it possible to take into account the fact that each protein may be involved into several cellular functions

BAYESIAN NONPARAMETRIC CROSS-STUDY VALIDATION OF PREDICTION METHODS

We consider comparisons of statistical learning algorithms using multiple data sets, via leave-one-in cross-study validation: each of the algorithms is trained on one data set; the resulting model is then validated on each remaining data set. This poses two statistical challenges that need to be addressed simultaneously. The first is the assessment of study heterogeneity, with the aim of identifying a subset of studies within which algorithm comparisons can be reliably carried out. The second is the comparison of algorithms using the ensemble of data sets. We address both problems by integrating clustering and model comparison. We formulate a Bayesian model for the array of cross-study validation statistics, which defines clusters of studies with similar properties and provides the basis for meaningful algorithm comparison in the presence of study heterogeneity. We illustrate our approach through simulations involving studies with varying severity of systematic errors, and in the context of medical prognosis for patients diagnosed with cancer, using high-throughput measurements of the transcriptional activity of the tumor’s genes

Renewing Felsenstein’s phylogenetic Bootstrap in the era of big data

Felsenstein’s application of the bootstrap method to evolutionary trees is one of the most cited scientific papers of all time. The bootstrap method, which is based on resampling and replications, is used extensively to assess the robustness of phylogenetic inferences. However, increasing numbers of sequences are now available for a wide variety of species, and phylogenies based on hundreds or thousands of taxa are becoming routine. With phylogenies of this size Felsenstein’s bootstrap tends to yield very low supports, especially on deep branches. Here we propose a new version of the phylogenetic bootstrap in which the presence of inferred branches in replications is measured using a gradual ‘transfer’ distance rather than the binary presence or absence index used in Felsenstein’s original version. The resulting supports are higher and do not induce falsely supported branches. The application of our method to large mammal, HIV and simulated datasets reveals their phylogenetic signals, whereas Felsenstein’s bootstrap fails to do so

Transfer distance between partitions

Partition, Distance, Clustering, Transfer graph, Centrality, 03E02, 05A18, 05C12, 05C35, 05D99, 62G15, 62G30,