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Biological Networks for Cancer Candidate Biomarkers Discovery
Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating om...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Libertas Academica
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5012434/ https://www.ncbi.nlm.nih.gov/pubmed/27625573 http://dx.doi.org/10.4137/CIN.S39458 |
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author | Yan, Wenying Xue, Wenjin Chen, Jiajia Hu, Guang |
author_facet | Yan, Wenying Xue, Wenjin Chen, Jiajia Hu, Guang |
author_sort | Yan, Wenying |
collection | PubMed |
description | Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field. |
format | Online Article Text |
id | pubmed-5012434 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Libertas Academica |
record_format | MEDLINE/PubMed |
spelling | pubmed-50124342016-09-13 Biological Networks for Cancer Candidate Biomarkers Discovery Yan, Wenying Xue, Wenjin Chen, Jiajia Hu, Guang Cancer Inform Review Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field. Libertas Academica 2016-09-04 /pmc/articles/PMC5012434/ /pubmed/27625573 http://dx.doi.org/10.4137/CIN.S39458 Text en © 2016 the author(s), publisher and licensee Libertas Academica Ltd. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 license. |
spellingShingle | Review Yan, Wenying Xue, Wenjin Chen, Jiajia Hu, Guang Biological Networks for Cancer Candidate Biomarkers Discovery |
title | Biological Networks for Cancer Candidate Biomarkers Discovery |
title_full | Biological Networks for Cancer Candidate Biomarkers Discovery |
title_fullStr | Biological Networks for Cancer Candidate Biomarkers Discovery |
title_full_unstemmed | Biological Networks for Cancer Candidate Biomarkers Discovery |
title_short | Biological Networks for Cancer Candidate Biomarkers Discovery |
title_sort | biological networks for cancer candidate biomarkers discovery |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5012434/ https://www.ncbi.nlm.nih.gov/pubmed/27625573 http://dx.doi.org/10.4137/CIN.S39458 |
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