Article ID Journal Published Year Pages File Type
552574 Decision Support Systems 2007 19 Pages PDF
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.

Related Topics
Physical Sciences and Engineering Computer Science Information Systems
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