Link test-A statistical method for finding prostate cancer biomarkers

Xutao Deng, Huimin Geng, Dhundy Raj Bastola, Hesham H Ali

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

We present a new method, link-test, to select prostate cancer biomarkers from SELDI mass spectrometry and microarray data sets. Biomarkers selected by link-test are supported by data sets from both mRNA and protein levels, and therefore results in improved robustness. Link-test determines the level of significance of the association between a microarray marker and a specific mass spectrum marker by constructing background mass spectra distributions estimated by all human protein sequences in the SWISS-PROT database. The data set consist of both microarray and mass spectrometry data from prostate cancer patients and healthy controls. A list of statistically justified prostate cancer biomarkers is reported by link-test. Cross-validation results show high prediction accuracy using the identified biomarker panel. We also employ a text-mining approach with OMIM database to validate the cancer biomarkers. The study with link-test represents one of the first cross-platform studies of cancer biomarkers.

Original languageEnglish (US)
Pages (from-to)425-433
Number of pages9
JournalComputational Biology and Chemistry
Volume30
Issue number6
DOIs
StatePublished - Dec 1 2006

Fingerprint

Prostate Cancer
biomarkers
Biomarkers
Tumor Biomarkers
Statistical method
Prostatic Neoplasms
Statistical methods
Microarrays
cancer
Mass spectrometry
Mass Spectrometry
Databases
Proteins
Genetic Databases
Microarray
markers
Protein Databases
mass spectra
Data Mining
Cancer

Keywords

  • Biomarker
  • Mass spectrometry
  • Microarray
  • Prostate cancer
  • Text mining

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Organic Chemistry
  • Computational Mathematics

Cite this

Link test-A statistical method for finding prostate cancer biomarkers. / Deng, Xutao; Geng, Huimin; Bastola, Dhundy Raj; Ali, Hesham H.

In: Computational Biology and Chemistry, Vol. 30, No. 6, 01.12.2006, p. 425-433.

Research output: Contribution to journalArticle

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