Journal
DECISION SUPPORT SYSTEMS
Volume 43, Issue 4, Pages 1207-1225Publisher
ELSEVIER
DOI: 10.1016/j.dss.2006.02.002
Keywords
genetic regulatory networks; microarray; Bayesian networks; association rules
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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.
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