The purpose of this thesis is the use of current transcriptomic data in order to infer a gene regulatory network. These data are often complex, and in particular intervention data may be present. The use of causality theory makes it possible to use these interventions to obtain acyclic causal networks. I question the notion of acyclicity, then based on this theory, I propose several algorithms and / or improvements to current techniques to use this type of data.L'objet de cette thèse est l'utilisation de données transcriptomiques actuelles dans le but d'en inférer un réseau de régulation génique. Ces données sont souvent complexes, et en particulier des données d'interventions peuvent être présente. L'utilisation de la théorie de la causali...
International audienceComplex diseases such as Cancer or Alzheimer's are caused by multiple molecula...
Diagnostic reasoning (abductive) and predictive reasoning (inductive) are two methods of reasoning t...
AbstractUnderstanding causal relationships among large numbers of variables is a fundamental goal of...
L'objet de cette thèse est l'utilisation de données transcriptomiques actuelles dans le but d'en inf...
Causal network inference is an important methodological challenge in biology as well as other areas ...
Causal network inference is an important methodological challenge in biology as well as other areas ...
International audienceCausal network inference is an important methodological challenge in biology a...
Thesis: S.M., Massachusetts Institute of Technology, Department of Biological Engineering, February,...
L'inférence de la causalité est une problématique récurrente pour un large éventail de domaines où l...
L’algorithme développé durant ma thèse utilise la théorie de l’information pour l’apprentissage d’un...
Background: Inference and understanding of gene networks from experimental data is an important but ...
International audienceWhen a signal triggers a cell, the inherent genetic program is activated, lead...
Through their transcript products genes regulate the rates at which an immense variety of transcript...
L'inférence des réseaux de régulation de gènes (RRG) à partir de données d'expression est un défi ma...
Background: In recent years, there has been great interest in using transcriptomic data to infer gen...
International audienceComplex diseases such as Cancer or Alzheimer's are caused by multiple molecula...
Diagnostic reasoning (abductive) and predictive reasoning (inductive) are two methods of reasoning t...
AbstractUnderstanding causal relationships among large numbers of variables is a fundamental goal of...
L'objet de cette thèse est l'utilisation de données transcriptomiques actuelles dans le but d'en inf...
Causal network inference is an important methodological challenge in biology as well as other areas ...
Causal network inference is an important methodological challenge in biology as well as other areas ...
International audienceCausal network inference is an important methodological challenge in biology a...
Thesis: S.M., Massachusetts Institute of Technology, Department of Biological Engineering, February,...
L'inférence de la causalité est une problématique récurrente pour un large éventail de domaines où l...
L’algorithme développé durant ma thèse utilise la théorie de l’information pour l’apprentissage d’un...
Background: Inference and understanding of gene networks from experimental data is an important but ...
International audienceWhen a signal triggers a cell, the inherent genetic program is activated, lead...
Through their transcript products genes regulate the rates at which an immense variety of transcript...
L'inférence des réseaux de régulation de gènes (RRG) à partir de données d'expression est un défi ma...
Background: In recent years, there has been great interest in using transcriptomic data to infer gen...
International audienceComplex diseases such as Cancer or Alzheimer's are caused by multiple molecula...
Diagnostic reasoning (abductive) and predictive reasoning (inductive) are two methods of reasoning t...
AbstractUnderstanding causal relationships among large numbers of variables is a fundamental goal of...