Reconstruction of gene networks using Bayesian learning and manipulation experiments.
BACKGROUND
The analysis of high-throughput experimental data, for example from microarray experiments, is currently seen as a promising way of finding regulatory relationships between genes. Bayesian networks have been suggested for learning gene regulatory networks from observational data. Not all causal relationships can be inferred from correlation data alone. Often several equivalent but different directed graphs explain the data equally well. Intervention experiments where genes are manipulated can help to narrow down the range of possible networks.


RESULTS
We describe an active learning algorithm that suggests an optimized sequence of intervention experiments. Simulation experiments show that our selection scheme is better than an unguided choice of interventions in learning the correct network and compares favorably in running time and results with methods based on value of information calculations.
Pournara
Iosifina
I
Department of Crystallography, Birkbeck College, University of London, Malet Street, London, WC1E 7HX, UK.
Wernisch
Lorenz
L
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000378:metabolism*; D009369:Neoplasms / Q000503:physiopathology*; D015398:Signal Transduction / Q000502:physiology*
D009363:Neoplasm Proteins