Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual informa

Background: Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mechanistic relationships from quantitative biological data. In this work we introduce a new statistical learning strategy, MI3 that addresses three common issues in previous methods simultaneously: (1) handling of continuous variables, (2) detection of more complex three-way relationships and (3) better differentiation of causal versus confounding relationships. With these improvements, we provide a more realistic representation of the underlying biological system. Results: We test the MI3 algorithm using both synthetic and experimental data. In the synthetic data experiment, MI3 achieved an absolute sensitivity/precision of 0.77/0.83 and a relative sensitivity/precision both of 0.99. In addition, MI3 significantly outperformed the control methods, including Bayesian networks, classical two-way mutual information...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Weijun Luo, Kurt D. Hankenson, Peter J. Woolf
• Keywords: Bayesian Networks, BMCBI 2008, Control Methods, Statistical Learning