Integrating heterogeneous microarray data sources using correlation signatures

Jaewoo Kang, Jiong Yang, Wanhong Xu, Pankaj Chopra

Research output: Contribution to journalConference articlepeer-review

13 Citations (Scopus)


Microarrays are one of the latest breakthroughs in experimental molecular biology. Thousands of different research groups generate tens of thousands of microarray gene expression profiles based on different tissues, species, and conditions. Combining such vast amount of microarray data sets is an important and yet challenging problem. In this paper, we introduce a "correlation signature" method that allows the coherent interpretation and integration of microarray data across disparate sources. The proposed algorithm first builds, for each gene (row) in a table, a correlation signature that captures the system-wide dependencies existing between the gene and the other genes within the table, and then compares the signatures across the tables for further analysis. We validate our framework with an experimental study using real microarray data sets, the result of which suggests that such an approach can be a viable solution for the microarray data integration and analysis problems.

Original languageEnglish
Pages (from-to)105-120
Number of pages16
JournalLecture Notes in Bioinformatics (Subseries of Lecture Notes in Computer Science)
Publication statusPublished - 2005
Externally publishedYes
EventSecond International Workshop on Data Integration in the Life Sciences, DILS 2005 - San Diego, CA, United States
Duration: 2005 Jul 202005 Jul 22

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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