Discovering relational-based association rules with multiple minimum supports on microarray datasets | Zendy

Yucheng Liu | Zendy; Chun-Pei Cheng | Zendy; Vincent S. Tseng | Zendy

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Discovering relational-based association rules with multiple minimum supports on microarray datasets

Author(s) -

Yucheng Liu,

Chun-Pei Cheng,

Vincent S. Tseng

Publication year - 2011

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btr526

Subject(s) - association rule learning , computer science , association (psychology) , data mining , relational database , microarray analysis techniques , biology , gene , psychology , genetics , psychotherapist , gene expression

Association rule analysis methods are important techniques applied to gene expression data for finding expression relationships between genes. However, previous methods implicitly assume that all genes have similar importance, or they ignore the individual importance of each gene. The relation intensity between any two items has never been taken into consideration. Therefore, we proposed a technique named REMMAR (RElational-based Multiple Minimum supports Association Rules) algorithm to tackle this problem. This method adjusts the minimum relation support (MRS) for each gene pair depending on the regulatory relation intensity to discover more important association rules with stronger biological meaning.

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