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Bioinformatics Advance Access published online on July 29, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth448
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received March 4, 2004
Revised June 18, 2004
Accepted July 13, 2004

Article

Advances to Bayesian network inference for generating causal networks from observational biological data

Jing Yu 1*, V. Anne Smith 2, Paul P. Wang 3, Alexander J. Hartemink 4, Erich D. Jarvis 2

1 Duke University Medical Center, Department of Neurobiology, Box 3209, Durham, NC 27710; Duke University, Department of Electrical Engineering, Box 90291, Durham, NC 27708
2 Duke University Medical Center, Department of Neurobiology, Box 3209, Durham, NC 27710
3 Duke University, Department of Electrical Engineering, Box 90291, Durham, NC 27708
4 Duke University, Department of Computer Science, Box 90129, Durham, NC 27708

* To whom correspondence should be addressed. E-mail: yu{at}ee.duke.edu.


  Abstract

Motivation: Network inference algorithms are powerful computational tools for identifying potential causal interactions among variables from observational data. Bayesian network inference algorithms hold particular promise in that they can capture linear, non-linear, combinatorial, stochastic, and other types of relationships among variables across multiple levels of biological organization. However, challenges remain when applying these algorithms to limited quantities of experimental data collected from biological systems. Here, we use a simulation approach to make advances in our dynamic Bayesian network (DBN) inference algorithm, especially in the context of limited quantities of biological data.

Results: We test a range of scoring metrics and search heuristics to find an effective algorithm configuration for evaluating our methodological advances. We also identify sampling intervals and levels of data discretization that allow best recovery of the simulated networks. We develop a novel influence score for DBNs that attempts to estimate both the sign (activation or repression) and relative magnitude of interactions among variables. When faced with limited quantities of observational data, combining our influence score with moderate data interpolation reduces a significant portion of false positive interactions in the recovered networks. Together, our advances allow DBN inference algorithms to be more effective in recovering biological networks from experimentally collected data.

Availability: Source code and simulated data are available upon request.

Supplemental material: http://www.jarvislab.net/Bioinformatics/BNAdvances/.


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