BioMed Research International

BioMed Research International / 2003 / Article
Special Issue

Proteomics in Health and Disease — Part II

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Research article | Open Access

Volume 2003 |Article ID 231929 | https://doi.org/10.1155/S1110724303210032

Antonia Vlahou, John O. Schorge, Betsy W. Gregory, Robert L. Coleman, "Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data", BioMed Research International, vol. 2003, Article ID 231929, 7 pages, 2003. https://doi.org/10.1155/S1110724303210032

Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data

Received24 Oct 2002
Revised16 Feb 2003
Accepted19 Feb 2003

Abstract

Recent reports from our laboratory and others support the SELDI ProteinChip technology as a potential clinical diagnostic tool when combined with n-dimensional analyses algorithms. The objective of this study was to determine if the commercially available classification algorithm biomarker patterns software (BPS), which is based on a classification and regression tree (CART), would be effective in discriminating ovarian cancer from benign diseases and healthy controls. Serum protein mass spectrum profiles from 139 patients with either ovarian cancer, benign pelvic diseases, or healthy women were analyzed using the BPS software. A decision tree, using five protein peaks resulted in an accuracy of 81.5% in the cross-validation analysis and 80%in a blinded set of samples in differentiating the ovarian cancer from the control groups. The potential, advantages, and drawbacks of the BPS system as a bioinformatic tool for the analysis of the SELDI high-dimensional proteomic data are discussed.

Copyright © 2003 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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