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 Citation (Scopus)

Abstract

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
Volume84
Issue number2
DOIs
StatePublished - Feb 2014

Fingerprint

Association Rule Mining
Association rules
Microarrays
Microarray Data
Genes
Gene
Association Rules
DNA Microarray
Gene Regulation
Relationships
Strategy
Association rule mining
Microarray
Resolve
Set theory
Gene expression
Binary
DNA
Subset

Keywords

  • 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

Cite this

An extended association rule mining strategy for gene relationship discovery from microarray data. / Peng, Bin; Zhu, Dianwen; Yang, Xiaowei; Liu, Ling; Huang, Wenquan; Zhou, Xiaohua; Yi, Dong.

In: Journal of Statistical Computation and Simulation, Vol. 84, No. 2, 02.2014, p. 384-396.

Research output: Contribution to journalArticle

Peng, Bin ; Zhu, Dianwen ; Yang, Xiaowei ; Liu, Ling ; Huang, Wenquan ; Zhou, Xiaohua ; Yi, Dong. / An extended association rule mining strategy for gene relationship discovery from microarray data. In: Journal of Statistical Computation and Simulation. 2014 ; Vol. 84, No. 2. pp. 384-396.
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