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Simultaneous identification of robust synergistic subnetwork markers for effective cancer prognosis

BACKGROUND: Accurate prediction of cancer prognosis based on gene expression data is generally difficult, and identifying robust prognostic markers for cancer remains a challenging problem. Recent studies have shown that modular markers, such as pathway markers and subnetwork markers, can provide be...

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Detalles Bibliográficos
Autores principales: Khunlertgit, Navadon, Yoon, Byung-Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5270447/
https://www.ncbi.nlm.nih.gov/pubmed/28194169
http://dx.doi.org/10.1186/s13637-014-0019-9
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author Khunlertgit, Navadon
Yoon, Byung-Jun
author_facet Khunlertgit, Navadon
Yoon, Byung-Jun
author_sort Khunlertgit, Navadon
collection PubMed
description BACKGROUND: Accurate prediction of cancer prognosis based on gene expression data is generally difficult, and identifying robust prognostic markers for cancer remains a challenging problem. Recent studies have shown that modular markers, such as pathway markers and subnetwork markers, can provide better snapshots of the underlying biological mechanisms by incorporating additional biological information, thereby leading to more accurate cancer classification. RESULTS: In this paper, we propose a novel method for simultaneously identifying robust synergistic subnetwork markers that can accurately predict cancer prognosis. The proposed method utilizes an efficient message-passing algorithm called affinity propagation, based on which we identify groups – or subnetworks – of discriminative and synergistic genes, whose protein products are closely located in the protein-protein interaction (PPI) network. Unlike other existing subnetwork marker identification methods, our proposed method can simultaneously identify multiple nonoverlapping subnetwork markers that can synergistically predict cancer prognosis. CONCLUSIONS: Evaluation results based on multiple breast cancer datasets demonstrate that the proposed message-passing approach can identify robust subnetwork markers in the human PPI network, which have higher discriminative power and better reproducibility compared to those identified by previous methods. The identified subnetwork makers can lead to better cancer classifiers with improved overall performance and consistency across independent cancer datasets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13637-014-0019-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-52704472017-02-13 Simultaneous identification of robust synergistic subnetwork markers for effective cancer prognosis Khunlertgit, Navadon Yoon, Byung-Jun EURASIP J Bioinform Syst Biol Research BACKGROUND: Accurate prediction of cancer prognosis based on gene expression data is generally difficult, and identifying robust prognostic markers for cancer remains a challenging problem. Recent studies have shown that modular markers, such as pathway markers and subnetwork markers, can provide better snapshots of the underlying biological mechanisms by incorporating additional biological information, thereby leading to more accurate cancer classification. RESULTS: In this paper, we propose a novel method for simultaneously identifying robust synergistic subnetwork markers that can accurately predict cancer prognosis. The proposed method utilizes an efficient message-passing algorithm called affinity propagation, based on which we identify groups – or subnetworks – of discriminative and synergistic genes, whose protein products are closely located in the protein-protein interaction (PPI) network. Unlike other existing subnetwork marker identification methods, our proposed method can simultaneously identify multiple nonoverlapping subnetwork markers that can synergistically predict cancer prognosis. CONCLUSIONS: Evaluation results based on multiple breast cancer datasets demonstrate that the proposed message-passing approach can identify robust subnetwork markers in the human PPI network, which have higher discriminative power and better reproducibility compared to those identified by previous methods. The identified subnetwork makers can lead to better cancer classifiers with improved overall performance and consistency across independent cancer datasets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13637-014-0019-9) contains supplementary material, which is available to authorized users. Springer International Publishing 2014-11-06 /pmc/articles/PMC5270447/ /pubmed/28194169 http://dx.doi.org/10.1186/s13637-014-0019-9 Text en © Khunlertgit and Yoon; licensee Springer. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
spellingShingle Research
Khunlertgit, Navadon
Yoon, Byung-Jun
Simultaneous identification of robust synergistic subnetwork markers for effective cancer prognosis
title Simultaneous identification of robust synergistic subnetwork markers for effective cancer prognosis
title_full Simultaneous identification of robust synergistic subnetwork markers for effective cancer prognosis
title_fullStr Simultaneous identification of robust synergistic subnetwork markers for effective cancer prognosis
title_full_unstemmed Simultaneous identification of robust synergistic subnetwork markers for effective cancer prognosis
title_short Simultaneous identification of robust synergistic subnetwork markers for effective cancer prognosis
title_sort simultaneous identification of robust synergistic subnetwork markers for effective cancer prognosis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5270447/
https://www.ncbi.nlm.nih.gov/pubmed/28194169
http://dx.doi.org/10.1186/s13637-014-0019-9
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