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

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 threedimensional 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.