### Abstract

Lingua originale | English |
---|---|

pagine (da-a) | - |

Numero di pagine | 16 |

Rivista | Statistical Applications in Genetics and Molecular Biology |

Volume | 00 |

Stato di pubblicazione | Published - 2014 |

Pubblicato esternamente | Yes |

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*Statistical Applications in Genetics and Molecular Biology*,

*00*, -.

**Model selection for structured dynamic gene regulatory networks of Neisseria meningitis.** / Vinciotti, V; Augugliaro, L; Abbruzzo, A; Saunders, N; Wit, E.

Risultato della ricerca: Article

*Statistical Applications in Genetics and Molecular Biology*, vol. 00, pagg. -.

}

TY - JOUR

T1 - Model selection for structured dynamic gene regulatory networks of Neisseria meningitis

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

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10447/101820

M3 - Article

VL - 00

SP - -

JO - Statistical Applications in Genetics and Molecular Biology

JF - Statistical Applications in Genetics and Molecular Biology

SN - 1544-6115

ER -