Skip to Main content Skip to Navigation
Journal articles

Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues

George Michailidis 1 Florence d'Alché-Buc 2, 3
3 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : Reconstructing gene regulatory networks from high-throughput measurements represents a key problem in functional genomics. It also represents a canonical learning problem and thus has attracted a lot of attention in both the informatics and the statistical learning literature. Numerous approaches have been proposed, ranging from simple clustering to rather involved dynamic Bayesian network modeling, as well as hybrid ones that combine a number of modeling steps, such as employing ordinary differential equations coupled with genome annotation. These approaches are tailored to the type of data being employed. Available data sources include static steady state data and time course data obtained either for wild type phenotypes or from perturbation experiments. This review focuses on the class of autoregressive models using time course data for inferring gene regulatory networks. The central themes of sparsity, stability and causality are discussed as well as the ability to integrate prior knowledge for successful use of these models for the learning task at hand.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-00909809
Contributor : Frédéric Davesne <>
Submitted on : Tuesday, November 26, 2013 - 9:03:20 PM
Last modification on : Tuesday, June 30, 2020 - 11:56:07 AM

Identifiers

Collections

Citation

George Michailidis, Florence d'Alché-Buc. Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues. Mathematical Biosciences, Elsevier, 2013, 246 (2), pp.326--334. ⟨10.1016/j.mbs.2013.10.003⟩. ⟨hal-00909809⟩

Share

Metrics

Record views

468