NDLI: Classification Analysis of Surface-enhanced Laser Desorption/Ionization Mass Spectral Serum Profiles for Prostate Cancer

Content Provider	IEEE Xplore Digital Library
Author	Peterson, L.E. Hoogeveen, R.C. Pownall, H.J. Morrisett, J.D.
Copyright Year	2006
Description	Author affiliation: Baylor Coll. of Medicine, Houston (Peterson, L.E.; Hoogeveen, R.C.; Pownall, H.J.; Morrisett, J.D.)
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.
Starting Page	3828
Ending Page	3835
File Size	385033
Page Count	8
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.246877
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Surface emitting lasers Ionization Prostate cancer Niobium compounds Support vector machines Support vector machine classification Performance analysis Proteins Entropy Linear discriminant analysis
Content Type	Text
Resource Type	Article

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