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Identifying network biomarkers based on protein-protein interactions and expression data

Identifying effective biomarkers to battle complex diseases is an important but challenging task in biomedical research today. Molecular data of complex diseases is increasingly abundant due to the rapid advance of high throughput technologies. However, a great gap remains in identifying the massive...

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Detalles Bibliográficos
Autores principales: Xin, Jingxue, Ren, Xianwen, Chen, Luonan, Wang, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460625/
https://www.ncbi.nlm.nih.gov/pubmed/26044366
http://dx.doi.org/10.1186/1755-8794-8-S2-S11
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author Xin, Jingxue
Ren, Xianwen
Chen, Luonan
Wang, Yong
author_facet Xin, Jingxue
Ren, Xianwen
Chen, Luonan
Wang, Yong
author_sort Xin, Jingxue
collection PubMed
description Identifying effective biomarkers to battle complex diseases is an important but challenging task in biomedical research today. Molecular data of complex diseases is increasingly abundant due to the rapid advance of high throughput technologies. However, a great gap remains in identifying the massive molecular data to phenotypic changes, in particular, at a network level, i.e., a novel method for identifying network biomarkers is in pressing need to accurately classify and diagnose diseases from molecular data and shed light on the mechanisms of disease pathogenesis. Rather than seeking differential genes at an individual-molecule level, here we propose a novel method for identifying network biomarkers based on protein-protein interaction affinity (PPIA), which identify the differential interactions at a network level. Specifically, we firstly define PPIAs by estimating the concentrations of protein complexes based on the law of mass action upon gene expression data. Then we select a small and non-redundant group of protein-protein interactions and single proteins according to the PPIAs, that maximizes the discerning ability of cases from controls. This method is mathematically formulated as a linear programming, which can be efficiently solved and guarantees a globally optimal solution. Extensive results on experimental data in breast cancer demonstrate the effectiveness and efficiency of the proposed method for identifying network biomarkers, which not only can accurately distinguish the phenotypes but also provides significant biological insights at a network or pathway level. In addition, our method provides a new way to integrate static protein-protein interaction information with dynamical gene expression data.
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spelling pubmed-44606252015-06-29 Identifying network biomarkers based on protein-protein interactions and expression data Xin, Jingxue Ren, Xianwen Chen, Luonan Wang, Yong BMC Med Genomics Research Identifying effective biomarkers to battle complex diseases is an important but challenging task in biomedical research today. Molecular data of complex diseases is increasingly abundant due to the rapid advance of high throughput technologies. However, a great gap remains in identifying the massive molecular data to phenotypic changes, in particular, at a network level, i.e., a novel method for identifying network biomarkers is in pressing need to accurately classify and diagnose diseases from molecular data and shed light on the mechanisms of disease pathogenesis. Rather than seeking differential genes at an individual-molecule level, here we propose a novel method for identifying network biomarkers based on protein-protein interaction affinity (PPIA), which identify the differential interactions at a network level. Specifically, we firstly define PPIAs by estimating the concentrations of protein complexes based on the law of mass action upon gene expression data. Then we select a small and non-redundant group of protein-protein interactions and single proteins according to the PPIAs, that maximizes the discerning ability of cases from controls. This method is mathematically formulated as a linear programming, which can be efficiently solved and guarantees a globally optimal solution. Extensive results on experimental data in breast cancer demonstrate the effectiveness and efficiency of the proposed method for identifying network biomarkers, which not only can accurately distinguish the phenotypes but also provides significant biological insights at a network or pathway level. In addition, our method provides a new way to integrate static protein-protein interaction information with dynamical gene expression data. BioMed Central 2015-05-29 /pmc/articles/PMC4460625/ /pubmed/26044366 http://dx.doi.org/10.1186/1755-8794-8-S2-S11 Text en Copyright © 2015 Xin et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 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 cited. 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
Xin, Jingxue
Ren, Xianwen
Chen, Luonan
Wang, Yong
Identifying network biomarkers based on protein-protein interactions and expression data
title Identifying network biomarkers based on protein-protein interactions and expression data
title_full Identifying network biomarkers based on protein-protein interactions and expression data
title_fullStr Identifying network biomarkers based on protein-protein interactions and expression data
title_full_unstemmed Identifying network biomarkers based on protein-protein interactions and expression data
title_short Identifying network biomarkers based on protein-protein interactions and expression data
title_sort identifying network biomarkers based on protein-protein interactions and expression data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460625/
https://www.ncbi.nlm.nih.gov/pubmed/26044366
http://dx.doi.org/10.1186/1755-8794-8-S2-S11
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