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2007

Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining

13 years 2 months ago
Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining
We present two algorithms for learning large-scale gene regulatory networks from microarray data: a modified informationtheory-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. © 2006 Elsevier B.V. All rights reserved.
Zan Huang, Jiexun Li, Hua Su, George S. Watts, Hsi
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2007
Where DSS
Authors Zan Huang, Jiexun Li, Hua Su, George S. Watts, Hsinchun Chen
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