Classification and capture of regulation networks with bayesian networks in oncology
type: Dataset
description: In oncology, microarrays have become a classical tool to search and characterize pathologies at a deeper level than previous methods, using genetic expression to find the mechanisms, classes, molecular associations, and cellular interaction networks of different cancers. From a biological point of view, these cellular networks are interesting because they concentrate a large amount of knowledge about cellular processes. The goal of this PhD thesis project is to extract structures that could correspond to genetic interaction networks from the expression data. "Bayesian Networks", i.e. a graphic and probabilistic method that models even static systems (like the expression network) with conditional independences, are used as the framework to investigate this problem. The adaptation of this method to data where the dimension of the variables (about 105 for gene expression) is much greater than the dimension of the samples (about 102 in oncology) aggravates some statistical and combinatorial problems. For several cancer problematics, this project proposes an acceleration strategy for capturing expression networks with Bayesian Networks and some methods to classify tumors, finding gene signatures of particular biological conditions by searching for local networks in the neighborhood of a gene of interest. In parallel, we propose to model a Bayesian Network from a known biological network, which is useful to simulate samples and to test these methods to reconstruct graphs from
landing page: https://theses.hal.science/tel-00707732
identifier: NNT: 2010LYO10065
keyword: bayesian-networks
keyword: cancer
keyword: cellular-networks
keyword: classification
keyword: gene-selection
keyword: infoeu-reposemanticsdoctoralthesis
keyword: reseaux-bayesiens
keyword: reseaux-cellulaires
keyword: sdvsalife-sciences-q-bioagricultural-sciences
keyword: selection-de-variables
keyword: theses
keyword: transcriptome
issued: 2026-05-15T18:24:27.239564
modified: 2026-05-15T18:24:27.239569
language: fr
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Classification and capture of regulation networks with bayesian networks in oncology
type: Distribution
format: HTML
access URL: https://theses.hal.science/tel-00707732
issued: 2026-05-15T18:24:27.285677
modified: 2026-05-15T18:24:27.201642