Proteomics in Health and Disease Part IIView this Special Issue
Research article | Open Access
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
Recent reports from our laboratory and others support the SELDI ProteinChip technology as a potential clinical diagnostic tool when combined with -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.