A novel method for mining temporally dependent association rules in three-dimensional microarray datasets

Yu Cheng Liu*, Chao Hui Lee, Wei Chung Chen, J. W. Shin, Hui Huang Hsu, S. Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

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.

Original languageEnglish
Title of host publicationICS 2010 - International Computer Symposium
Pages759-764
Number of pages6
DOIs
StatePublished - 1 Dec 2010
Event2010 International Computer Symposium, ICS 2010 - Tainan, Taiwan
Duration: 16 Dec 201018 Dec 2010

Publication series

NameICS 2010 - International Computer Symposium

Conference

Conference2010 International Computer Symposium, ICS 2010
CountryTaiwan
CityTainan
Period16/12/1018/12/10

Keywords

  • Association rule mining
  • Data mining
  • Gene expression analysis
  • Microarray

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