Exploiting inter-gene information for microarray data integration

Kuan Ming Lin, Jaewoo Kang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


Microarray data integration is an important yet challenging problem. Usually, direct integration of microarrays after normalization is ineective because of the diverse types of experiment specic variations. To address this issue, two novel integration approaches were proposed in recent microarray studies. Therst study[16] presented a cancer classication technique which identies gene pairs whose expression orders are consistent within class and dierent across classes. The other study[18] presented a promising gene expression analysis technique which utilizes pairwise correlations of gene expressions across dierent microarray datasets. Interestingly, we observe that both of the independently developed techniques rely on inter-gene nformation and noise ltering strategy to achieve satisfactory performance in microarray integration. Motivated by this observation, we propose in this paper a formal data model for microarray integration using inter-gene information and effective ltering, which generalizes the previous two frameworks. We also show how the proposed model can handle a broader range of problems than the previous frameworks.

Original languageEnglish
Title of host publicationProceedings of the 2007 ACM Symposium on Applied Computing
PublisherAssociation for Computing Machinery
Number of pages5
ISBN (Print)1595934804, 9781595934802
Publication statusPublished - 2007
Event2007 ACM Symposium on Applied Computing - Seoul, Korea, Republic of
Duration: 2007 Mar 112007 Mar 15

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Other2007 ACM Symposium on Applied Computing
Country/TerritoryKorea, Republic of


  • Biological data integration
  • Biomarker identification
  • Gene clustering
  • Gene interrelation
  • Microarray analysis

ASJC Scopus subject areas

  • Software


Dive into the research topics of 'Exploiting inter-gene information for microarray data integration'. Together they form a unique fingerprint.

Cite this