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 n-dimensional analyses algorithms. The objective of this study was to determine if the commercially available classification algorithm biomarker patterns...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2003-01-01
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| Series: | Journal of Biomedicine and Biotechnology |
| Online Access: | http://dx.doi.org/10.1155/S1110724303210032 |
| Summary: | 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. |
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| ISSN: | 1110-7243 1110-7251 |