Gene regulatory networks are made of highly tuned, sparse and dynamical operations. We consider the case of the Neisseria meningitidis bacterium, a causative agent of life-threatening infections such as meningitis, and aim to infer a robust net- work of interactions across sixty proteins based on a detailed time course gene expres- sion study. We consider the problem of estimating a sparse dynamic Gaussian graphical model with L1 penalized maximum likelihood under a structured precision matrix. The structure can consist of specific time dynamics, known presence or absence of links in the graphical model or equality constraints on the parameters. The authors developed a new optimization algorithm for constrained penalized maximum likelihood, which returns a sequence of networks along a solution path. In this paper, we propose a gener- alized cross-validation approach to select a suitable penalty parameter and a bootstrap sampling approach to robustify the network.
Vinciotti, V., Augugliaro, L., Abbruzzo, A., Wit, E. (2014). Robustness of dynamic gene regulatory networks in Neisseria. In Proceedings of the Eleventh international Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics.
Robustness of dynamic gene regulatory networks in Neisseria
AUGUGLIARO, Luigi;ABBRUZZO, Antonino;
2014-01-01
Abstract
Gene regulatory networks are made of highly tuned, sparse and dynamical operations. We consider the case of the Neisseria meningitidis bacterium, a causative agent of life-threatening infections such as meningitis, and aim to infer a robust net- work of interactions across sixty proteins based on a detailed time course gene expres- sion study. We consider the problem of estimating a sparse dynamic Gaussian graphical model with L1 penalized maximum likelihood under a structured precision matrix. The structure can consist of specific time dynamics, known presence or absence of links in the graphical model or equality constraints on the parameters. The authors developed a new optimization algorithm for constrained penalized maximum likelihood, which returns a sequence of networks along a solution path. In this paper, we propose a gener- alized cross-validation approach to select a suitable penalty parameter and a bootstrap sampling approach to robustify the network.File | Dimensione | Formato | |
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