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

Research output: Contribution to journalArticle

14 Citations (Scopus)

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 2008

Fingerprint

Organ Specificity
Data Mining
Biomarkers
Data mining
Tissue
Gene expression
Expressed Sequence Tags
Gene Expression
Microarrays
High-Throughput Nucleotide Sequencing
Assays
Genes
Throughput
Databases

ASJC Scopus subject areas

  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

Data Mining for Biomarker Development : A Review of Tissue Specificity Analysis. / Klee, Eric W.

In: Clinics in Laboratory Medicine, Vol. 28, No. 1, 03.2008, p. 127-143.

Research output: Contribution to journalArticle

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