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Kernel methods for gene regulatory network inference

Abstract : New technologies in molecular biology, in particular dna microarrays, have greatly increased the quantity of available data. in this context, methods from mathematics and computer science have been actively developed to extract information from large datasets. in particular, the problem of gene regulatory network inference has been tackled using many different mathematical and statistical models, from the most basic ones (correlation, boolean or linear models) to the most elaborate (regression trees, bayesian models with latent variables). despite their qualities when applied to similar problems, kernel methods have scarcely been used for gene network inference, because of their lack of interpretability. in this thesis, two approaches are developed to obtain interpretable kernel methods. firstly, from a theoretical point of view, some kernel methods are shown to consistently estimate a transition function and its partial derivatives from a learning dataset. these estimations of partial derivatives allow to better infer the gene regulatory network than previous methods on realistic gene regulatory networks. secondly, an interpretable kernel methods through multiple kernel learning is presented. this method, called lockni, provides state-of-the-art results on real and realistically simulated datasets.
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Submitted on : Thursday, May 31, 2018 - 3:29:49 PM
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  • HAL Id : tel-01804286, version 1


Arnaud Fouchet. Kernel methods for gene regulatory network inference. Bioinformatics [q-bio.QM]. Université d'Evry-Val-d'Essonne, 2014. English. ⟨NNT : 2014EVRY0058⟩. ⟨tel-01804286⟩



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