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.
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