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Network-Based Logistic Classification with an Enhanced L (1/2) Solver Reveals Biomarker and Subnetwork Signatures for Diagnosing Lung Cancer

Identifying biomarker and signaling pathway is a critical step in genomic studies, in which the regularization method is a widely used feature extraction approach. However, most of the regularizers are based on L (1)-norm and their results are not good enough for sparsity and interpretation and are...

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Autores principales: Huang, Hai-Hui, Liang, Yong, Liu, Xiao-Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4488258/
https://www.ncbi.nlm.nih.gov/pubmed/26185761
http://dx.doi.org/10.1155/2015/713953
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author Huang, Hai-Hui
Liang, Yong
Liu, Xiao-Ying
author_facet Huang, Hai-Hui
Liang, Yong
Liu, Xiao-Ying
author_sort Huang, Hai-Hui
collection PubMed
description Identifying biomarker and signaling pathway is a critical step in genomic studies, in which the regularization method is a widely used feature extraction approach. However, most of the regularizers are based on L (1)-norm and their results are not good enough for sparsity and interpretation and are asymptotically biased, especially in genomic research. Recently, we gained a large amount of molecular interaction information about the disease-related biological processes and gathered them through various databases, which focused on many aspects of biological systems. In this paper, we use an enhanced L (1/2) penalized solver to penalize network-constrained logistic regression model called an enhanced L (1/2) net, where the predictors are based on gene-expression data with biologic network knowledge. Extensive simulation studies showed that our proposed approach outperforms L (1) regularization, the old L (1/2) penalized solver, and the Elastic net approaches in terms of classification accuracy and stability. Furthermore, we applied our method for lung cancer data analysis and found that our method achieves higher predictive accuracy than L (1) regularization, the old L (1/2) penalized solver, and the Elastic net approaches, while fewer but informative biomarkers and pathways are selected.
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spelling pubmed-44882582015-07-16 Network-Based Logistic Classification with an Enhanced L (1/2) Solver Reveals Biomarker and Subnetwork Signatures for Diagnosing Lung Cancer Huang, Hai-Hui Liang, Yong Liu, Xiao-Ying Biomed Res Int Research Article Identifying biomarker and signaling pathway is a critical step in genomic studies, in which the regularization method is a widely used feature extraction approach. However, most of the regularizers are based on L (1)-norm and their results are not good enough for sparsity and interpretation and are asymptotically biased, especially in genomic research. Recently, we gained a large amount of molecular interaction information about the disease-related biological processes and gathered them through various databases, which focused on many aspects of biological systems. In this paper, we use an enhanced L (1/2) penalized solver to penalize network-constrained logistic regression model called an enhanced L (1/2) net, where the predictors are based on gene-expression data with biologic network knowledge. Extensive simulation studies showed that our proposed approach outperforms L (1) regularization, the old L (1/2) penalized solver, and the Elastic net approaches in terms of classification accuracy and stability. Furthermore, we applied our method for lung cancer data analysis and found that our method achieves higher predictive accuracy than L (1) regularization, the old L (1/2) penalized solver, and the Elastic net approaches, while fewer but informative biomarkers and pathways are selected. Hindawi Publishing Corporation 2015 2015-06-16 /pmc/articles/PMC4488258/ /pubmed/26185761 http://dx.doi.org/10.1155/2015/713953 Text en Copyright © 2015 Hai-Hui Huang 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
Huang, Hai-Hui
Liang, Yong
Liu, Xiao-Ying
Network-Based Logistic Classification with an Enhanced L (1/2) Solver Reveals Biomarker and Subnetwork Signatures for Diagnosing Lung Cancer
title Network-Based Logistic Classification with an Enhanced L (1/2) Solver Reveals Biomarker and Subnetwork Signatures for Diagnosing Lung Cancer
title_full Network-Based Logistic Classification with an Enhanced L (1/2) Solver Reveals Biomarker and Subnetwork Signatures for Diagnosing Lung Cancer
title_fullStr Network-Based Logistic Classification with an Enhanced L (1/2) Solver Reveals Biomarker and Subnetwork Signatures for Diagnosing Lung Cancer
title_full_unstemmed Network-Based Logistic Classification with an Enhanced L (1/2) Solver Reveals Biomarker and Subnetwork Signatures for Diagnosing Lung Cancer
title_short Network-Based Logistic Classification with an Enhanced L (1/2) Solver Reveals Biomarker and Subnetwork Signatures for Diagnosing Lung Cancer
title_sort network-based logistic classification with an enhanced l (1/2) solver reveals biomarker and subnetwork signatures for diagnosing lung cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4488258/
https://www.ncbi.nlm.nih.gov/pubmed/26185761
http://dx.doi.org/10.1155/2015/713953
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