Data Mining for Biomarker Development: A Review of Tissue Specificity Analysis

Research output: Contribution to journalReview article

15 Scopus citations

Abstract

Novel biomarker development requires a significant resource commitment to translate candidate markers into clinical assays. Consequently, it is imperative high quality candidates are selected early in a biomarker development program. High throughput gene expression data are routinely used to identify transcripts differentially expressed in diseased versus normal samples. Data-mining Expressed Sequence Tag, Serial Analysis of Gene Expression, Massively Parallel Signature Sequencing, and microarray expression databases can provide additional information on the expression of candidate biomarkers across multiple tissues, organs, and disease states. From this information, quantitative measures of tissue-specific gene specificity are computed and used to guide candidate biomarker selection.

Original languageEnglish (US)
Pages (from-to)127-143
Number of pages17
JournalClinics in Laboratory Medicine
Volume28
Issue number1
DOIs
StatePublished - Mar 1 2008

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Biochemistry, medical

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