An extended association rule mining strategy for gene relationship discovery from microarray data

Bin Peng, Dianwen Zhu, Xiaowei Yang, Ling Liu, Wenquan Huang, Xiaohua Zhou, Dong Yi

Research output: Contribution to journalArticle

1 Scopus citations


DNA microarrays allow for measuring expression levels of a large number of genes between different experimental conditions and/or samples. Association rule mining (ARM) methods are helpful in finding associational relationships between genes. However, classical association rule mining (CARM) algorithms extract only a subset of the associations that exist among different binary states; therefore can only infer part of the relationships on gene regulations. To resolve this problem, we developed an extended association rule mining (EARM) strategy along with a new way of the association rule definition. Compared with the CARM method, our new approach extracted more frequent genesets from a public microarray data set. The EARM method discovered some biologically interesting association rules that were not detected by CARM. Therefore, EARM provides an effective tool for exploring relationships among genes.

Original languageEnglish (US)
Pages (from-to)384-396
Number of pages13
JournalJournal of Statistical Computation and Simulation
Issue number2
Publication statusPublished - Feb 2014



  • extended association rule mining
  • gene expression
  • yeast microarray data set

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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