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Regularized binormal ROC method in disease classification using microarray data
BACKGROUND: An important application of microarrays is to discover genomic biomarkers, among tens of thousands of genes assayed, for disease diagnosis and prognosis. Thus it is of interest to develop efficient statistical methods that can simultaneously identify important biomarkers from such high-t...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2006
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1513612/ https://www.ncbi.nlm.nih.gov/pubmed/16684357 http://dx.doi.org/10.1186/1471-2105-7-253 |
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author | Ma, Shuangge Song, Xiao Huang, Jian |
author_facet | Ma, Shuangge Song, Xiao Huang, Jian |
author_sort | Ma, Shuangge |
collection | PubMed |
description | BACKGROUND: An important application of microarrays is to discover genomic biomarkers, among tens of thousands of genes assayed, for disease diagnosis and prognosis. Thus it is of interest to develop efficient statistical methods that can simultaneously identify important biomarkers from such high-throughput genomic data and construct appropriate classification rules. It is also of interest to develop methods for evaluation of classification performance and ranking of identified biomarkers. RESULTS: The ROC (receiver operating characteristic) technique has been widely used in disease classification with low dimensional biomarkers. Compared with the empirical ROC approach, the binormal ROC is computationally more affordable and robust in small sample size cases. We propose using the binormal AUC (area under the ROC curve) as the objective function for two-sample classification, and the scaled threshold gradient directed regularization method for regularized estimation and biomarker selection. Tuning parameter selection is based on V-fold cross validation. We develop Monte Carlo based methods for evaluating the stability of individual biomarkers and overall prediction performance. Extensive simulation studies show that the proposed approach can generate parsimonious models with excellent classification and prediction performance, under most simulated scenarios including model mis-specification. Application of the method to two cancer studies shows that the identified genes are reasonably stable with satisfactory prediction performance and biologically sound implications. The overall classification performance is satisfactory, with small classification errors and large AUCs. CONCLUSION: In comparison to existing methods, the proposed approach is computationally more affordable without losing the optimality possessed by the standard ROC method. |
format | Text |
id | pubmed-1513612 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-15136122006-07-24 Regularized binormal ROC method in disease classification using microarray data Ma, Shuangge Song, Xiao Huang, Jian BMC Bioinformatics Methodology Article BACKGROUND: An important application of microarrays is to discover genomic biomarkers, among tens of thousands of genes assayed, for disease diagnosis and prognosis. Thus it is of interest to develop efficient statistical methods that can simultaneously identify important biomarkers from such high-throughput genomic data and construct appropriate classification rules. It is also of interest to develop methods for evaluation of classification performance and ranking of identified biomarkers. RESULTS: The ROC (receiver operating characteristic) technique has been widely used in disease classification with low dimensional biomarkers. Compared with the empirical ROC approach, the binormal ROC is computationally more affordable and robust in small sample size cases. We propose using the binormal AUC (area under the ROC curve) as the objective function for two-sample classification, and the scaled threshold gradient directed regularization method for regularized estimation and biomarker selection. Tuning parameter selection is based on V-fold cross validation. We develop Monte Carlo based methods for evaluating the stability of individual biomarkers and overall prediction performance. Extensive simulation studies show that the proposed approach can generate parsimonious models with excellent classification and prediction performance, under most simulated scenarios including model mis-specification. Application of the method to two cancer studies shows that the identified genes are reasonably stable with satisfactory prediction performance and biologically sound implications. The overall classification performance is satisfactory, with small classification errors and large AUCs. CONCLUSION: In comparison to existing methods, the proposed approach is computationally more affordable without losing the optimality possessed by the standard ROC method. BioMed Central 2006-05-09 /pmc/articles/PMC1513612/ /pubmed/16684357 http://dx.doi.org/10.1186/1471-2105-7-253 Text en Copyright © 2006 Ma et al; licensee BioMed Central Ltd. https://creativecommons.org/licenses/by/2.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0 (https://creativecommons.org/licenses/by/2.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Ma, Shuangge Song, Xiao Huang, Jian Regularized binormal ROC method in disease classification using microarray data |
title | Regularized binormal ROC method in disease classification using microarray data |
title_full | Regularized binormal ROC method in disease classification using microarray data |
title_fullStr | Regularized binormal ROC method in disease classification using microarray data |
title_full_unstemmed | Regularized binormal ROC method in disease classification using microarray data |
title_short | Regularized binormal ROC method in disease classification using microarray data |
title_sort | regularized binormal roc method in disease classification using microarray data |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1513612/ https://www.ncbi.nlm.nih.gov/pubmed/16684357 http://dx.doi.org/10.1186/1471-2105-7-253 |
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