Gene networks inference using dynamic Bayesian networks.
This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed. It can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement. Parameters of the model are learned through a penalized likelihood maximization implemented through an extended version of EM algorithm. Our approach is tested against experimental data relative to the S.O.S. DNA Repair network of the Escherichia coli bacterium. It appears to be able to extract the main regulations between the genes involved in this network. An added missing variable is found to model the main protein of the network. Good prediction abilities on unlearned data are observed. These first results are very promising: they show the power of the learning algorithm and the ability of the model to capture gene interactions.
Perrin
Bruno-Edouard
BE
Laboratoire d'Informatique de Paris 6, CNRS UMR 7606, Paris, France. perrin@poleia.lip6.fr
Ralaivola
Liva
L
Mazurie
Aurélien
A
Bottani
Samuele
S
Mallet
Jacques
J
d'Alché-Buc
Florence
F
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D004260:DNA Repair / Q000502:physiology*; D003627:Data Interpretation, Statistical; D004926:Escherichia coli / Q000502:physiology*; D029968:Escherichia coli Proteins / Q000378:metabolism*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D010363:Pattern Recognition, Automated; D015398:Signal Transduction / Q000502:physiology*
D029968:Escherichia coli Proteins