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Bioinformatics Advance Access published online on July 21, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm372
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

miniTUBA: medical inference by network integration of temporal data using Bayesian analysis

Zuoshuang Xiang 1,4,5, Rebecca M. Minter 2, Xiaoming Bi 2, Peter Woolf 3,5 and Yongqun He 1,4,5,*

1Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI.
2Department of Surgery, University of Michigan, Ann Arbor, MI.
3Department of Chemical Engineering, University of Michigan, Ann Arbor, MI.
4Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI.
5Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI.

*To whom correspondence should be addressed. Dr. Yongqun He, E-mail: yongqunh{at}med.umich.edu


  Abstract

Motivation: Many biomedical and clinical research problems involve discovering causal relationships between observations gathered from temporal events. Dynamic Bayesian networks are a powerful modeling approach to describe causal or apparently causal relationships, and support complex medical inference such as future response prediction, automated learning, and rational decision making. Although many engines exist for creating Bayesian networks, most require a local installation and significant data manipulation to be practical for a general biologist or clinician. No software pipeline currently exists for interpretation and inference of dynamic Bayesian networks learned from biomedical and clinical data.

Results: miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g., Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data.

Availability: miniTUBA is available at http://www.minituba.org.

Contact: yongqunh{at}umich.edu

Associate Editor: Dr. Jonathan Wren

Received on March 19, 2007; revised on July 10, 2007; accepted on July 11, 2007

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