Mining fuzzy association patterns in gene expression databases

Vincent S. Tseng, Yen Hsu Chen, Chun Hao Chen, J. W. Shin

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)87-93
期刊International Journal of Fuzzy Systems
出版狀態Published - 2006 6月

All Science Journal Classification (ASJC) codes

  • 理論電腦科學
  • 軟體
  • 計算機理論與數學
  • 人工智慧


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