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STOVEN, V.

Publications and source records attributed to STOVEN, V..

2 recordsLinked to original sources

From CFTR to a CF signalling network: a systems biology approach to study Cystic fibrosis

BackgroundCystic Fibrosis (CF) is a monogenic disease caused by mutations in the gene coding the Cystic Fibrosis Transmembrane Regulator (CFTR) protein, but its overall physio-pathology cannot be solely explained by the loss of the CFTR chloride channel function. Indeed, CFTR belongs to a yet not fully deciphered network of proteins participating in various signalling pathways. MethodsWe propose a systems biology approach to study how the absence of the CFTR protein at the membrane leads to perturbation of these pathways, resulting in a panel of deleterious CF cellular phenotypes. ResultsBased on publicly available transcriptomic datasets, we built and analyzed a CF network that recapitulates signalling dysregulations. The CF network topology and its resulting phenotype was found to be consistent with CF pathology. ConclusionAnalysis of the network topology highlighted a few proteins that may initiate the propagation of dysregulations, those that trigger CF cellular phenotypes, and suggested several candidate therapeutic targets. Although our research is focused on CF, the global approach proposed in the present paper could also be followed to study other rare monogenic diseases.

systems biology↗

Target identification of drug candidates with machine-learning algorithms: how to choose negative examples for training

(1) Background:Identification of hit molecules protein targets is essential in the drug discovery process. Target prediction with machine-learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positive predicted targets, thus increasing time and cost of experimental validation campaigns. (2) Methods: To minimize the number of false positive predicted proteins, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for 3 particular drugs, and more globally for 200 approved drugs. (3) Results: For the detailed 3 drugs examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positive among the top ranked predicted targets decreased and overall the rank of the true targets was improved. (4) Conclusion: Our method enables to correct databases statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken.

bioinformatics↗