A Novel Method for Mining Temporally Dependent Association Rules in Three-Dimensional Microarray Datasets

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Title:	A Novel Method for Mining Temporally Dependent Association Rules in Three-Dimensional Microarray Datasets
Authors:	Liu, Yu-cheng;Lee, Chao-hui;Chen, Wei-chung;Shin, J. W.;Hsu, Hui-huang;Tseng, Vincent S.
Contributors:	淡江大學資訊工程學系
Keywords:	Data Mining;Microarray;Gene Expression Analysis;Association Rule Mining
Date:	2010-12
Issue Date:	2012-04-16 09:42:50 (UTC+8)
Publisher:	Institute of Electrical and Electronics Engineers(IEEE)
Abstract:	Microarray data analysis is a very popular topic of current studies in bioinformatics. Most of the existing methods are focused on clustering-related approaches. However, the relations of genes cannot be generated by clustering mining. Some studies explored association rule mining on microarray, but there is no concrete framework proposed on three-dimensional gene-sample-time microarray datasets yet. In this paper, we proposed a temporal dependency association rule mining method named 3D-TDAR-Mine for three-dimensional analyzing microarray datasets. The mined rules can represent the regulated-relations between genes. Through experimental evaluation, our proposed method can discover the meaningful temporal dependent association rules that are really useful for biologists.
Relation:	Proc. 2010 International Computer Symposium, pp. 759-764
DOI:	10.1109/COMPSYM.2010.5685410
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

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