Prediction of body fluids where proteins are secreted into based on protein interaction network.

Determining the body fluids where secreted proteins can be secreted into is important for protein function annotation and disease biomarker discovery. In this study, we developed a network-based method to predict which kind of body fluids human proteins can be secreted into. For a newly constructed...

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Autores principales:	Le-Le Hu, Tao Huang, Yu-Dong Cai, Kuo-Chen Chou
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/600819998cfa4385b251f620af559b84
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spelling	oai:doaj.org-article:600819998cfa4385b251f620af559b842021-11-18T06:48:58ZPrediction of body fluids where proteins are secreted into based on protein interaction network.1932-620310.1371/journal.pone.0022989https://doaj.org/article/600819998cfa4385b251f620af559b842011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21829572/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Determining the body fluids where secreted proteins can be secreted into is important for protein function annotation and disease biomarker discovery. In this study, we developed a network-based method to predict which kind of body fluids human proteins can be secreted into. For a newly constructed benchmark dataset that consists of 529 human-secreted proteins, the prediction accuracy for the most possible body fluid location predicted by our method via the jackknife test was 79.02%, significantly higher than the success rate by a random guess (29.36%). The likelihood that the predicted body fluids of the first four orders contain all the true body fluids where the proteins can be secreted into is 62.94%. Our method was further demonstrated with two independent datasets: one contains 57 proteins that can be secreted into blood; while the other contains 61 proteins that can be secreted into plasma/serum and were possible biomarkers associated with various cancers. For the 57 proteins in first dataset, 55 were correctly predicted as blood-secrete proteins. For the 61 proteins in the second dataset, 58 were predicted to be most possible in plasma/serum. These encouraging results indicate that the network-based prediction method is quite promising. It is anticipated that the method will benefit the relevant areas for both basic research and drug development.Le-Le HuTao HuangYu-Dong CaiKuo-Chen ChouPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 7, p e22989 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Le-Le Hu Tao Huang Yu-Dong Cai Kuo-Chen Chou Prediction of body fluids where proteins are secreted into based on protein interaction network.
description	Determining the body fluids where secreted proteins can be secreted into is important for protein function annotation and disease biomarker discovery. In this study, we developed a network-based method to predict which kind of body fluids human proteins can be secreted into. For a newly constructed benchmark dataset that consists of 529 human-secreted proteins, the prediction accuracy for the most possible body fluid location predicted by our method via the jackknife test was 79.02%, significantly higher than the success rate by a random guess (29.36%). The likelihood that the predicted body fluids of the first four orders contain all the true body fluids where the proteins can be secreted into is 62.94%. Our method was further demonstrated with two independent datasets: one contains 57 proteins that can be secreted into blood; while the other contains 61 proteins that can be secreted into plasma/serum and were possible biomarkers associated with various cancers. For the 57 proteins in first dataset, 55 were correctly predicted as blood-secrete proteins. For the 61 proteins in the second dataset, 58 were predicted to be most possible in plasma/serum. These encouraging results indicate that the network-based prediction method is quite promising. It is anticipated that the method will benefit the relevant areas for both basic research and drug development.
format	article
author	Le-Le Hu Tao Huang Yu-Dong Cai Kuo-Chen Chou
author_facet	Le-Le Hu Tao Huang Yu-Dong Cai Kuo-Chen Chou
author_sort	Le-Le Hu
title	Prediction of body fluids where proteins are secreted into based on protein interaction network.
title_short	Prediction of body fluids where proteins are secreted into based on protein interaction network.
title_full	Prediction of body fluids where proteins are secreted into based on protein interaction network.
title_fullStr	Prediction of body fluids where proteins are secreted into based on protein interaction network.
title_full_unstemmed	Prediction of body fluids where proteins are secreted into based on protein interaction network.
title_sort	prediction of body fluids where proteins are secreted into based on protein interaction network.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/600819998cfa4385b251f620af559b84
work_keys_str_mv	AT lelehu predictionofbodyfluidswhereproteinsaresecretedintobasedonproteininteractionnetwork AT taohuang predictionofbodyfluidswhereproteinsaresecretedintobasedonproteininteractionnetwork AT yudongcai predictionofbodyfluidswhereproteinsaresecretedintobasedonproteininteractionnetwork AT kuochenchou predictionofbodyfluidswhereproteinsaresecretedintobasedonproteininteractionnetwork
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Prediction of body fluids where proteins are secreted into based on protein interaction network.

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