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Selecting differentially expressed proteomic markers from mass spectrometry data

  • Stony Brook University
  • New York University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

High-throughput mass spectrometry and statistical analysis methodologies are promising technologies to aid the medical diagnostics field by detecting the cancer-related proteomic markers. We propose statistical methods to cull the potential markers by ranking them in relations to their power of separability distinguishing cancerous patients from normal persons or among different cancer stages. To assess the training variability, resampling via bootstrap strategy is adopted to select stable markers which show the potential of a large probability to classify specific groups. Selected marker pattern is validated by a combined classifier. Methods are demonstrated by a colon cancer dataset screened by SELDI technology.

Original languageEnglish
Title of host publicationProceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Pages4775-4778
Number of pages4
StatePublished - 2005
Event2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 - Shanghai, China
Duration: Sep 1 2005Sep 4 2005

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume7 VOLS
ISSN (Print)0589-1019

Conference

Conference2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Country/TerritoryChina
CityShanghai
Period09/1/0509/4/05

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