Improving gene expression similarity measurement using pathway-based analytic dimension

Changwon Keum, Jung H. Woo, Won S. Oh, Sue Nie Park, Kyoung T. No

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Background: Gene expression similarity measuring methods were developed and applied to search rapidly growing public microarray databases. However, current expression similarity measuring methods need to be improved to accurately measure similarity between gene expression profiles from different platforms or different experiments. Results: We devised new gene expression similarity measuring method based on pathway information. In short, newly devised method measure similarity between gene expression profiles after converting them into pathway based expression profiles. To evaluate pathway based gene expression similarity measuring method, we conducted cell type classification test. Pathway based similarity measuring method shows higher classification accuracy. Especially, pathway based methods outperform at most 50% and 10% over conventional gene expression similarity method when search databases are limited to cross-platform profiles and cross-experiment profiles. Conclusion: The pathway based gene expression similarity measuring method outperforms commonly used similarity measuring methods. Considering the fact that public microarray database is consist of gene expression profiles of various experiments with various type of platform, pathway based gene expression similarity measuring method could be successfully applied for searching large public microarray databases.

Original languageEnglish
Article numberS15
JournalBMC Genomics
Volume10
Issue numberSUPPL. 3
DOIs
Publication statusPublished - 2009 Dec 3

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Gene Expression
Transcriptome
Databases

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Genetics

Cite this

Keum, Changwon ; Woo, Jung H. ; Oh, Won S. ; Park, Sue Nie ; No, Kyoung T. / Improving gene expression similarity measurement using pathway-based analytic dimension. In: BMC Genomics. 2009 ; Vol. 10, No. SUPPL. 3.
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Improving gene expression similarity measurement using pathway-based analytic dimension. / Keum, Changwon; Woo, Jung H.; Oh, Won S.; Park, Sue Nie; No, Kyoung T.

In: BMC Genomics, Vol. 10, No. SUPPL. 3, S15, 03.12.2009.

Research output: Contribution to journalArticle

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N2 - Background: Gene expression similarity measuring methods were developed and applied to search rapidly growing public microarray databases. However, current expression similarity measuring methods need to be improved to accurately measure similarity between gene expression profiles from different platforms or different experiments. Results: We devised new gene expression similarity measuring method based on pathway information. In short, newly devised method measure similarity between gene expression profiles after converting them into pathway based expression profiles. To evaluate pathway based gene expression similarity measuring method, we conducted cell type classification test. Pathway based similarity measuring method shows higher classification accuracy. Especially, pathway based methods outperform at most 50% and 10% over conventional gene expression similarity method when search databases are limited to cross-platform profiles and cross-experiment profiles. Conclusion: The pathway based gene expression similarity measuring method outperforms commonly used similarity measuring methods. Considering the fact that public microarray database is consist of gene expression profiles of various experiments with various type of platform, pathway based gene expression similarity measuring method could be successfully applied for searching large public microarray databases.

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