Predicting protein phenotypes based on protein-protein interaction network.

<h4>Background</h4>Identifying associated phenotypes of proteins is a challenge of the modern genetics since the multifactorial trait often results from contributions of many proteins. Besides the high-through phenotype assays, the computational methods are alternative ways to identify t...

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Autores principales:	Lele Hu, Tao Huang, Xiao-Jun Liu, Yu-Dong Cai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/72cf81c388944a8a9eda5627c54bbb56
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id	oai:doaj.org-article:72cf81c388944a8a9eda5627c54bbb56
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spelling	oai:doaj.org-article:72cf81c388944a8a9eda5627c54bbb562021-11-18T06:57:30ZPredicting protein phenotypes based on protein-protein interaction network.1932-620310.1371/journal.pone.0017668https://doaj.org/article/72cf81c388944a8a9eda5627c54bbb562011-03-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21423698/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Identifying associated phenotypes of proteins is a challenge of the modern genetics since the multifactorial trait often results from contributions of many proteins. Besides the high-through phenotype assays, the computational methods are alternative ways to identify the phenotypes of proteins.<h4>Methodology/principal findings</h4>Here, we proposed a new method for predicting protein phenotypes in yeast based on protein-protein interaction network. Instead of only the most likely phenotype, a series of possible phenotypes for the query protein were generated and ranked according to the tethering potential score. As a result, the first order prediction accuracy of our method achieved 65.4% evaluated by Jackknife test of 1,267 proteins in budding yeast, much higher than the success rate (15.4%) of a random guess. And the likelihood of the first 3 predicted phenotypes including all the real phenotypes of the proteins was 70.6%.<h4>Conclusions/significance</h4>The candidate phenotypes predicted by our method provided useful clues for the further validation. In addition, the method can be easily applied to the prediction of protein associated phenotypes in other organisms.Lele HuTao HuangXiao-Jun LiuYu-Dong CaiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 3, p e17668 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lele Hu Tao Huang Xiao-Jun Liu Yu-Dong Cai Predicting protein phenotypes based on protein-protein interaction network.
description	<h4>Background</h4>Identifying associated phenotypes of proteins is a challenge of the modern genetics since the multifactorial trait often results from contributions of many proteins. Besides the high-through phenotype assays, the computational methods are alternative ways to identify the phenotypes of proteins.<h4>Methodology/principal findings</h4>Here, we proposed a new method for predicting protein phenotypes in yeast based on protein-protein interaction network. Instead of only the most likely phenotype, a series of possible phenotypes for the query protein were generated and ranked according to the tethering potential score. As a result, the first order prediction accuracy of our method achieved 65.4% evaluated by Jackknife test of 1,267 proteins in budding yeast, much higher than the success rate (15.4%) of a random guess. And the likelihood of the first 3 predicted phenotypes including all the real phenotypes of the proteins was 70.6%.<h4>Conclusions/significance</h4>The candidate phenotypes predicted by our method provided useful clues for the further validation. In addition, the method can be easily applied to the prediction of protein associated phenotypes in other organisms.
format	article
author	Lele Hu Tao Huang Xiao-Jun Liu Yu-Dong Cai
author_facet	Lele Hu Tao Huang Xiao-Jun Liu Yu-Dong Cai
author_sort	Lele Hu
title	Predicting protein phenotypes based on protein-protein interaction network.
title_short	Predicting protein phenotypes based on protein-protein interaction network.
title_full	Predicting protein phenotypes based on protein-protein interaction network.
title_fullStr	Predicting protein phenotypes based on protein-protein interaction network.
title_full_unstemmed	Predicting protein phenotypes based on protein-protein interaction network.
title_sort	predicting protein phenotypes based on protein-protein interaction network.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/72cf81c388944a8a9eda5627c54bbb56
work_keys_str_mv	AT lelehu predictingproteinphenotypesbasedonproteinproteininteractionnetwork AT taohuang predictingproteinphenotypesbasedonproteinproteininteractionnetwork AT xiaojunliu predictingproteinphenotypesbasedonproteinproteininteractionnetwork AT yudongcai predictingproteinphenotypesbasedonproteinproteininteractionnetwork
_version_	1718424172847169536

Predicting protein phenotypes based on protein-protein interaction network.

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