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Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining
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DOI：10.1016/j.dss.2006.02.002.png)
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Abstract
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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. (C) 2006 Elsevier B.V. All rights reserved.
Keywords:
genetic regulatory networks
microarray
Bayesian networks
association rules
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