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 causalité permet d'utiliser ces interventions afin d'obtenir des réseaux causaux acycliques. Je questionne la notion d'acyclicité, puis en m'appuyant sur cette théorie, je propose plusieurs algorithmes et/ou améliorations à des techniques actuelles permettant d'utiliser ce type de données particulières.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 interve...
Background: In recent years, there has been great interest in using transcriptomic data to infer gen...
ABSTRACT Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of...
© 2017 Neural information processing systems foundation. All rights reserved. Learning directed acyc...
The purpose of this thesis is the use of current transcriptomic data in order to infer a gene regula...
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...
Most machine learning-based methods predict outcomes rather than understanding causality. Machine le...
Background: Inference and understanding of gene networks from experimental data is an important but ...
In this paper, we present a systematic and conceptual overview of methods for inferring gene regulat...
Motivation: Prior biological knowledge greatly facilitates the mean-ingful interpretation of gene-ex...
Background: In recent years, there has been great interest in using transcriptomic data to infer gen...
Background: In recent years, there has been great interest in using transcriptomic data to infer gen...
ABSTRACT Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of...
© 2017 Neural information processing systems foundation. All rights reserved. Learning directed acyc...
The purpose of this thesis is the use of current transcriptomic data in order to infer a gene regula...
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...
Most machine learning-based methods predict outcomes rather than understanding causality. Machine le...
Background: Inference and understanding of gene networks from experimental data is an important but ...
In this paper, we present a systematic and conceptual overview of methods for inferring gene regulat...
Motivation: Prior biological knowledge greatly facilitates the mean-ingful interpretation of gene-ex...
Background: In recent years, there has been great interest in using transcriptomic data to infer gen...
Background: In recent years, there has been great interest in using transcriptomic data to infer gen...
ABSTRACT Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of...
© 2017 Neural information processing systems foundation. All rights reserved. Learning directed acyc...