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...

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Main Authors:	Antonia Vlahou, John O. Schorge, Betsy W. Gregory, Robert L. Coleman
Format:	Article
Language:	English
Published:	Hindawi Limited 2003-01-01
Series:	Journal of Biomedicine and Biotechnology
Online Access:	http://dx.doi.org/10.1155/S1110724303210032

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spelling	doaj-e4df2c47c01647d9bb9dc6ffbe0654dd2020-11-25T00:53:19ZengHindawi LimitedJournal of Biomedicine and Biotechnology1110-72431110-72512003-01-012003530831410.1155/S1110724303210032Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral DataAntonia Vlahou0John O. Schorge1Betsy W. Gregory2Robert L. Coleman3Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, VA 23501, USADivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Texas Southwestern, Dallas, TX 75390, USADepartment of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, VA 23501, USADivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Texas Southwestern, Dallas, TX 75390, USARecent 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.http://dx.doi.org/10.1155/S1110724303210032
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Antonia Vlahou John O. Schorge Betsy W. Gregory Robert L. Coleman
spellingShingle	Antonia Vlahou John O. Schorge Betsy W. Gregory Robert L. Coleman Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data Journal of Biomedicine and Biotechnology
author_facet	Antonia Vlahou John O. Schorge Betsy W. Gregory Robert L. Coleman
author_sort	Antonia Vlahou
title	Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data
title_short	Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data
title_full	Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data
title_fullStr	Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data
title_full_unstemmed	Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data
title_sort	diagnosis of ovarian cancer using decision tree classification of mass spectral data
publisher	Hindawi Limited
series	Journal of Biomedicine and Biotechnology
issn	1110-7243 1110-7251
publishDate	2003-01-01
description	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.
url	http://dx.doi.org/10.1155/S1110724303210032
work_keys_str_mv	AT antoniavlahou diagnosisofovariancancerusingdecisiontreeclassificationofmassspectraldata AT johnoschorge diagnosisofovariancancerusingdecisiontreeclassificationofmassspectraldata AT betsywgregory diagnosisofovariancancerusingdecisiontreeclassificationofmassspectraldata AT robertlcoleman diagnosisofovariancancerusingdecisiontreeclassificationofmassspectraldata
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Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data

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