Application of the GA/KNN method to SELDI proteomics data

Leping Li, David M. Umbach, Paul Terry, Jack A. Taylor

Research output: Contribution to journalArticle

62 Citations (Scopus)

Abstract

Summary: Proteomics technology has shown promise in identifying biomarkers for disease, toxicant exposure and stress. We show by example that the genetic algorithm/k-nearest neighbors method, developed for mining high-dimensional microarray gene expression data, is also capable of mining surface enhanced laser desorption/ionization-time-of-flight proteomics data.

Original languageEnglish (US)
Pages (from-to)1638-1640
Number of pages3
JournalBioinformatics
Volume20
Issue number10
DOIs
StatePublished - Jul 10 2004

Fingerprint

Proteomics
Mining
Toxicants
Nearest Neighbor Method
Open pit mining
Desorption
Time-of-flight
Biomarkers
Microarrays
Ionization
Gene Expression Data
Microarray Data
Gene expression
Lasers
High-dimensional
Genetic algorithms
Genetic Algorithm
Laser
Technology
Gene Expression

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Application of the GA/KNN method to SELDI proteomics data. / Li, Leping; Umbach, David M.; Terry, Paul; Taylor, Jack A.

In: Bioinformatics, Vol. 20, No. 10, 10.07.2004, p. 1638-1640.

Research output: Contribution to journalArticle

Li, Leping ; Umbach, David M. ; Terry, Paul ; Taylor, Jack A. / Application of the GA/KNN method to SELDI proteomics data. In: Bioinformatics. 2004 ; Vol. 20, No. 10. pp. 1638-1640.
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