Classification analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles for prostate cancer

Leif E. Peterson, Ron C. Hoogeveen, Henry J. Pownall, Joel D. Morrisett

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

5 Scopus citations

Abstract

Classification analysis was performed on 322 SELDI-TOF-MS protein expression profiles for prostate cancer. Feature ranking was based on the F-test, information gain (entropy), and Gini diversity applied in a pairwise, one-against-all, and all-at-once modular form. Classifiers included 4NN, NBC, LDA, LVQ1, SVM, and ANN. 4-class bootstrap (0.632) accuracies were in the range 50-80%, with NBC resulting in the lowest average accuracy (50-66%) and SVM resulting in the greatest average accuracy (71-79%). A 12-peak model with 88% accuracy collapsed into 6 peaks with m/z values of 3460, 4172, 4581, 6890, 14281 and 14696. The peaks identified may be confirmed in the future to be markers of early detection and/or therapy.

Original languageEnglish (US)
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3828-3835
Number of pages8
ISBN (Print)0780394909, 9780780394902
DOIs
StatePublished - 2006
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: Jul 16 2006Jul 21 2006

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Other

OtherInternational Joint Conference on Neural Networks 2006, IJCNN '06
CountryCanada
CityVancouver, BC
Period7/16/067/21/06

ASJC Scopus subject areas

  • Software

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