Mining fuzzy association patterns in gene expression databases

Vincent Shin-Mu Tseng*, Yen Hsu Chen, Chun Hao Chen, J. W. Shin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In this paper, we propose two fuzzy data mining approaches for microarray analysis, namely Fuzzy Associative Gene Expression (FAGE) and Ripple Effective Gene Expression Rule (REGER) algorithms. Both of them first transform microarray data into fuzzy items, and then use fuzzy operators and specially-designed data structures to discover the relationships among genes. Through the proposed algorithms, a novel pattern named Ripple Pattern is discovered that indicates the genes active at the same time with their linguistic terms being monotone increasing or decreasing. The experimental results show that the proposed algorithms are effective in discovering novel and useful rules from microarray data.

Original languageEnglish
Pages (from-to)87-93
Number of pages7
JournalInternational Journal of Fuzzy Systems
Volume8
Issue number2
DOIs
StatePublished - 1 Jun 2006

Keywords

  • Association Rule
  • Fuzzy Set
  • Gene Expression Analysis
  • Microarray
  • Ripple Pattern

Fingerprint Dive into the research topics of 'Mining fuzzy association patterns in gene expression databases'. Together they form a unique fingerprint.

Cite this