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Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer
BACKGROUD: To facilitate advances in personalized medicine, it is important to detect predictive, stable and interpretable biomarkers related with different clinical characteristics. These clinical characteristics may be heterogeneous with respect to underlying interactions between genes. Usually, t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4769543/ https://www.ncbi.nlm.nih.gov/pubmed/26921029 http://dx.doi.org/10.1186/s12859-016-0951-7 |
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author | Wu, Meng-Yun Zhang, Xiao-Fei Dai, Dao-Qing Ou-Yang, Le Zhu, Yuan Yan, Hong |
author_facet | Wu, Meng-Yun Zhang, Xiao-Fei Dai, Dao-Qing Ou-Yang, Le Zhu, Yuan Yan, Hong |
author_sort | Wu, Meng-Yun |
collection | PubMed |
description | BACKGROUD: To facilitate advances in personalized medicine, it is important to detect predictive, stable and interpretable biomarkers related with different clinical characteristics. These clinical characteristics may be heterogeneous with respect to underlying interactions between genes. Usually, traditional methods just focus on detection of differentially expressed genes without taking the interactions between genes into account. Moreover, due to the typical low reproducibility of the selected biomarkers, it is difficult to give a clear biological interpretation for a specific disease. Therefore, it is necessary to design a robust biomarker identification method that can predict disease-associated interactions with high reproducibility. RESULTS: In this article, we propose a regularized logistic regression model. Different from previous methods which focus on individual genes or modules, our model takes gene pairs, which are connected in a protein-protein interaction network, into account. A line graph is constructed to represent the adjacencies between pairwise interactions. Based on this line graph, we incorporate the degree information in the model via an adaptive elastic net, which makes our model less dependent on the expression data. Experimental results on six publicly available breast cancer datasets show that our method can not only achieve competitive performance in classification, but also retain great stability in variable selection. Therefore, our model is able to identify the diagnostic and prognostic biomarkers in a more robust way. Moreover, most of the biomarkers discovered by our model have been verified in biochemical or biomedical researches. CONCLUSIONS: The proposed method shows promise in the diagnosis of disease pathogenesis with different clinical characteristics. These advances lead to more accurate and stable biomarker discovery, which can monitor the functional changes that are perturbed by diseases. Based on these predictions, researchers may be able to provide suggestions for new therapeutic approaches. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-0951-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4769543 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-47695432016-02-28 Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer Wu, Meng-Yun Zhang, Xiao-Fei Dai, Dao-Qing Ou-Yang, Le Zhu, Yuan Yan, Hong BMC Bioinformatics Research Article BACKGROUD: To facilitate advances in personalized medicine, it is important to detect predictive, stable and interpretable biomarkers related with different clinical characteristics. These clinical characteristics may be heterogeneous with respect to underlying interactions between genes. Usually, traditional methods just focus on detection of differentially expressed genes without taking the interactions between genes into account. Moreover, due to the typical low reproducibility of the selected biomarkers, it is difficult to give a clear biological interpretation for a specific disease. Therefore, it is necessary to design a robust biomarker identification method that can predict disease-associated interactions with high reproducibility. RESULTS: In this article, we propose a regularized logistic regression model. Different from previous methods which focus on individual genes or modules, our model takes gene pairs, which are connected in a protein-protein interaction network, into account. A line graph is constructed to represent the adjacencies between pairwise interactions. Based on this line graph, we incorporate the degree information in the model via an adaptive elastic net, which makes our model less dependent on the expression data. Experimental results on six publicly available breast cancer datasets show that our method can not only achieve competitive performance in classification, but also retain great stability in variable selection. Therefore, our model is able to identify the diagnostic and prognostic biomarkers in a more robust way. Moreover, most of the biomarkers discovered by our model have been verified in biochemical or biomedical researches. CONCLUSIONS: The proposed method shows promise in the diagnosis of disease pathogenesis with different clinical characteristics. These advances lead to more accurate and stable biomarker discovery, which can monitor the functional changes that are perturbed by diseases. Based on these predictions, researchers may be able to provide suggestions for new therapeutic approaches. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-0951-7) contains supplementary material, which is available to authorized users. BioMed Central 2016-02-27 /pmc/articles/PMC4769543/ /pubmed/26921029 http://dx.doi.org/10.1186/s12859-016-0951-7 Text en © Wu et al. 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Wu, Meng-Yun Zhang, Xiao-Fei Dai, Dao-Qing Ou-Yang, Le Zhu, Yuan Yan, Hong Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer |
title | Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer |
title_full | Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer |
title_fullStr | Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer |
title_full_unstemmed | Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer |
title_short | Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer |
title_sort | regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4769543/ https://www.ncbi.nlm.nih.gov/pubmed/26921029 http://dx.doi.org/10.1186/s12859-016-0951-7 |
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