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

Zan Huang, Jiexun Li, Hua Su, George S. Watts, Hsinchun Chen

Research output: Contribution to journalArticle

37 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1207-1225
Number of pages19
JournalDecision Support Systems
Volume43
Issue number4
DOIs
StatePublished - Aug 1 2007

Keywords

  • Association rules
  • Bayesian networks
  • Genetic regulatory networks
  • Microarray

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

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