A consolidated approach to analyzing data from high-throughput protein microarrays with an application to immune response profiling in humans

Mariusz Lubomirski, Michael R. D'Andrea, Stanley M. Belkowski, Javier Cabrera, James M. Dixon, Dhammika Amaratunga

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Motivation: DNA microarrays are a well-known and established technology in biological and pharmaceutical research providing a wealth of information essential for understanding biological processes and aiding drug development. Protein microarrays are quickly emerging as a follow-up technology, which will also begin to experience rapid growth as the challenges in protein to spot methodologies are overcome. Like DNA microarrays, their protein counterparts produce large amounts of data that must be suitably analyzed in order to yield meaningful information that should eventually lead to novel drug targets and biomarkers. Although the statistical management of DNA microarray data has been well described, there is no available report that offers a successful consolidated approach to the analysis of high-throughput protein microarray data. We describe the novel application of a statistical methodology to analyze the data from an immune response profiling assay using human protein microarray with over 5000 proteins on each chip.

Original languageEnglish (US)
Pages (from-to)350-359
Number of pages10
JournalJournal of Computational Biology
Volume14
Issue number3
DOIs
StatePublished - Apr 2007

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

Keywords

  • Antibody
  • DNA microarrays
  • Protein microarrays
  • Random forest
  • Spectral map

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