Model selection for structured dynamic gene regulatory networks of Neisseria meningitis

Vinciotti, V; Augugliaro, L; Abbruzzo, A; Saunders, N; Wit, E

Risultato della ricerca: Articlepeer review

Abstract

Factorial graphical models have recently been proposed for inferring dynamic regulatory networks from high-throughput data. In the search of true regulatory relationships amongst the vast space of possible networks, these models allow to impose certain restrictions on the dynamic nature of these relationships, such as that Markov dependencies are of low order, i.e. some entries of the precision matrix are a priori zeros, or that the strength of the dependencies depend only on time lags, i.e. some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by $l_1$ penalised likelihood, imposing a further constraint on the absolute value of its entries, which results in sparse networks. The problem of selecting the optimal sparsity level is traditionally framed in terms of the Kullback-Leibler (KL) divergence. In this paper, we present a KL-motivated model selection criterion for factorial graphical models, by taking into account the a priori structural constraints. We test the performance of this method on simulated data and compare it with existing approaches. Finally, we present an application on a detailed time-course microarray data from the \textit{Neisseria meningitidis} bacterium, a causative agent of life-threatening infections such as meningitis.
Lingua originaleEnglish
pagine (da-a)-
Numero di pagine16
RivistaStatistical Applications in Genetics and Molecular Biology
Volume00
Stato di pubblicazionePublished - 2014
Pubblicato esternamenteYes

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