Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining |
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| Authors: | Zan Jiexun Hua George S Hsinchun |
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| Affiliation: | aDepartment of Supply Chain and Information Systems, Smeal College of Business, The Pennsylvania State University, University Park, PA 16802, United States;bDepartment of Management Information Systems, Eller College of Business and Public Administration, The University of Arizona, Tucson, AZ 85721, United States;cArizona Cancer Center and Department of Pharmacology and Toxicology, The University of Arizona, Tucson, AZ 85724, United States |
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| Abstract: | We present two algorithms for learning large-scale gene regulatory networks from microarray data: a modified information-theory-based Bayesian network algorithm and a modified association rule algorithm. Simulation-based evaluation using six datasets indicated that both algorithms outperformed their unmodified counterparts, especially when analyzing large numbers of genes. Both algorithms learned about 20% (50% if directionality and relation type were not considered) of the relations in the actual models. In our empirical evaluation based on two real datasets, domain experts evaluated subsets of learned relations with high confidence and identified 20–30% to be “interesting” or “maybe interesting” as potential experiment hypotheses. |
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| Keywords: | Genetic regulatory networks Microarray Bayesian networks Association rules |
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