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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2015
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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. |
format | Online Article Text |
id | pubmed-4488258 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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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