Information Systems Frontiers

, Volume 11, Issue 4, pp 433–447

Efficient mining of multilevel gene association rules from microarray and gene ontology

Authors

    • Department Computer Science and Information EngineeringNational Cheng Kung University
    • Institute of Medical InformaticsNational Cheng Kung University
  • Hsieh-Hui Yu
    • Department Computer Science and Information EngineeringNational Cheng Kung University
  • Shih-Chiang Yang
    • Department Computer Science and Information EngineeringNational Cheng Kung University
Article

DOI: 10.1007/s10796-009-9156-1

Cite this article as:
Tseng, V.S., Yu, H. & Yang, S. Inf Syst Front (2009) 11: 433. doi:10.1007/s10796-009-9156-1

Abstract

Some recent studies have shown that association rules can reveal the interactions between genes that might not have been revealed using traditional analysis methods like clustering. However, the existing studies consider only the association rules among individual genes. In this paper, we propose a new data mining method named MAGO for discovering the multilevel gene association rules from the gene microarray data and the concept hierarchy of Gene Ontology (GO). The proposed method can efficiently find out the relations between GO terms by analyzing the gene expressions with the hierarchy of GO. For example, with the biological process in GO, some rules like Process A (up) → Process B (up) cab be discovered, which indicates that the genes involved in Process B of GO are likely to be up-regulated whenever those involved in Process A are up-regulated. Moreover, we also propose a constrained mining method named CMAGO for discovering the multilevel gene expression rules with user-specified constraints. Through empirical evaluation, the proposed methods are shown to have excellent performance in discovering the hidden multilevel gene association rules.

Keywords

Data miningMicroarrayGene expression analysisAssociation rules miningMulti-level association rulesGene ontology

Copyright information

© Springer Science+Business Media, LLC 2009