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Modified Logistic Regression Models Using Gene Coexpression and Clinical Features to Predict Prostate Cancer Progression

Predicting disease progression is one of the most challenging problems in prostate cancer research. Adding gene expression data to prediction models that are based on clinical features has been proposed to improve accuracy. In the current study, we applied a logistic regression (LR) model combining...

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Autores principales: Zhao, Hongya, Logothetis, Christopher J., Gorlov, Ivan P., Zeng, Jia, Dai, Jianguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3866878/
https://www.ncbi.nlm.nih.gov/pubmed/24367394
http://dx.doi.org/10.1155/2013/917502
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author Zhao, Hongya
Logothetis, Christopher J.
Gorlov, Ivan P.
Zeng, Jia
Dai, Jianguo
author_facet Zhao, Hongya
Logothetis, Christopher J.
Gorlov, Ivan P.
Zeng, Jia
Dai, Jianguo
author_sort Zhao, Hongya
collection PubMed
description Predicting disease progression is one of the most challenging problems in prostate cancer research. Adding gene expression data to prediction models that are based on clinical features has been proposed to improve accuracy. In the current study, we applied a logistic regression (LR) model combining clinical features and gene co-expression data to improve the accuracy of the prediction of prostate cancer progression. The top-scoring pair (TSP) method was used to select genes for the model. The proposed models not only preserved the basic properties of the TSP algorithm but also incorporated the clinical features into the prognostic models. Based on the statistical inference with the iterative cross validation, we demonstrated that prediction LR models that included genes selected by the TSP method provided better predictions of prostate cancer progression than those using clinical variables only and/or those that included genes selected by the one-gene-at-a-time approach. Thus, we conclude that TSP selection is a useful tool for feature (and/or gene) selection to use in prognostic models and our model also provides an alternative for predicting prostate cancer progression.
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spelling pubmed-38668782013-12-23 Modified Logistic Regression Models Using Gene Coexpression and Clinical Features to Predict Prostate Cancer Progression Zhao, Hongya Logothetis, Christopher J. Gorlov, Ivan P. Zeng, Jia Dai, Jianguo Comput Math Methods Med Research Article Predicting disease progression is one of the most challenging problems in prostate cancer research. Adding gene expression data to prediction models that are based on clinical features has been proposed to improve accuracy. In the current study, we applied a logistic regression (LR) model combining clinical features and gene co-expression data to improve the accuracy of the prediction of prostate cancer progression. The top-scoring pair (TSP) method was used to select genes for the model. The proposed models not only preserved the basic properties of the TSP algorithm but also incorporated the clinical features into the prognostic models. Based on the statistical inference with the iterative cross validation, we demonstrated that prediction LR models that included genes selected by the TSP method provided better predictions of prostate cancer progression than those using clinical variables only and/or those that included genes selected by the one-gene-at-a-time approach. Thus, we conclude that TSP selection is a useful tool for feature (and/or gene) selection to use in prognostic models and our model also provides an alternative for predicting prostate cancer progression. Hindawi Publishing Corporation 2013 2013-12-04 /pmc/articles/PMC3866878/ /pubmed/24367394 http://dx.doi.org/10.1155/2013/917502 Text en Copyright © 2013 Hongya Zhao et al. https://creativecommons.org/licenses/by/3.0/ 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.
spellingShingle Research Article
Zhao, Hongya
Logothetis, Christopher J.
Gorlov, Ivan P.
Zeng, Jia
Dai, Jianguo
Modified Logistic Regression Models Using Gene Coexpression and Clinical Features to Predict Prostate Cancer Progression
title Modified Logistic Regression Models Using Gene Coexpression and Clinical Features to Predict Prostate Cancer Progression
title_full Modified Logistic Regression Models Using Gene Coexpression and Clinical Features to Predict Prostate Cancer Progression
title_fullStr Modified Logistic Regression Models Using Gene Coexpression and Clinical Features to Predict Prostate Cancer Progression
title_full_unstemmed Modified Logistic Regression Models Using Gene Coexpression and Clinical Features to Predict Prostate Cancer Progression
title_short Modified Logistic Regression Models Using Gene Coexpression and Clinical Features to Predict Prostate Cancer Progression
title_sort modified logistic regression models using gene coexpression and clinical features to predict prostate cancer progression
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3866878/
https://www.ncbi.nlm.nih.gov/pubmed/24367394
http://dx.doi.org/10.1155/2013/917502
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