Year: 2015
Type: article
Abstract: In tumoral cells, gene regulation mechanisms are severely altered. Genes that do not react normally to their regulators' activity can provide explanations for the tumoral behavior, and be characteristic of cancer subtypes. We thus propose a statistical methodology to identify the misregulated genes given a reference network and gene expression data. Our model is based on a regulatory process in which all genes are allowed to be deregulated. We derive an EM algorithm where the hidden variables correspond to the status (under/over/normally expressed) of the genes and where the E-step is solved thanks to a message passing algorithm. Our procedure provides posterior probabilities of deregulation in a given sample for each gene. We assess the performance of our method by numerical experiments on simulations and on a bladder cancer data set.
Source: BMC Systems Biology
Authors: Thomas Picchetti, Julien Chiquet, Mohamed Elati, Pierre Neuvial, Rémy Nicolle, Etienne Birmelé
Institutions: Centre National de la Recherche Scientifique, Sorbonne Paris Cité, Mathématiques Appliquées à Paris 5, Université Paris Descartes, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, École Nationale Supérieure d’Informatique pour l’Industrie et l’Entreprise, Laboratoire de Mathématiques et Modélisation d'Évry, Université d'Évry Val-d'Essonne, Institut de Biologie systémique et synthétique, Université Paris Sciences et Lettres, Biologie cellulaire et Cancer, Institut Curie
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,290
OpenAlex ID: W1847043871