Discovering biomarkers for myocardial infarction from SELDI-TOF spectra

Christian Höner Zu Siederdissen, Susanne Ragg, Sven Rahmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We describe a three-step procedure to separate patients with myocardial infarction from a control group based on SELDI-TOF mass spectra. The procedure returns features ("biomarkers") that are strongly present in one of the two groups. These features should allow future subjects to be classified as at-risk of myocardial infarction. The algorithm uses morphological operations to reduce noise in the input data as well as for performing baseline correction. In contrast to previous approaches on SELDI-TOF spectra, we avoid black-box machine learning procedures and use only features (protein masses) that are easy to interpret.

Original languageEnglish (US)
Title of host publicationAdvances in Data Analysis - Proceedings of the 30th Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKl 2006
Pages569-576
Number of pages8
StatePublished - Dec 1 2007
Event30th Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Advances in Data Analysis, GfKl 2006 - Berlin, Germany
Duration: Mar 8 2006Mar 10 2006

Publication series

NameStudies in Classification, Data Analysis, and Knowledge Organization
ISSN (Print)1431-8814

Other

Other30th Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Advances in Data Analysis, GfKl 2006
CountryGermany
CityBerlin
Period3/8/063/10/06

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ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Analysis

Cite this

Höner Zu Siederdissen, C., Ragg, S., & Rahmann, S. (2007). Discovering biomarkers for myocardial infarction from SELDI-TOF spectra. In Advances in Data Analysis - Proceedings of the 30th Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKl 2006 (pp. 569-576). (Studies in Classification, Data Analysis, and Knowledge Organization).