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Bioinformatics Advance Access originally published online on June 4, 2004
Bioinformatics 2004 20(17):2934-2942; doi:10.1093/bioinformatics/bth337
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Bioinformatics vol. 20 issue 17 © Oxford University Press 2004; all rights reserved.

Reconstruction of gene networks using Bayesian learning and manipulation experiments

Iosifina Pournara and Lorenz Wernisch *

Department of Crystallography, Birkbeck College, University of London, Malet Street, London, WC1E 7HX, UK

Received on December 4, 2003; revised on April 14, 2004; accepted on May 14, 2004
Advance Access Publication June 4, 2004

Motivation: 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.

Availability: Algorithms are available from the authors on request.

Contact: l.wernisch{at}bbk.ac.uk

* To whom correspondence should be addressed.


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