Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties.

<h4>Background</h4>With the huge amount of uncharacterized protein sequences generated in the post-genomic age, it is highly desirable to develop effective computational methods for quickly and accurately predicting their functions. The information thus obtained would be very useful for...

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Autores principales:	Lele Hu, Tao Huang, Xiaohe Shi, Wen-Cong Lu, 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/394cffc8da8c4a5cbcfefb9cda79db12
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spelling	oai:doaj.org-article:394cffc8da8c4a5cbcfefb9cda79db122021-11-18T07:00:17ZPredicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties.1932-620310.1371/journal.pone.0014556https://doaj.org/article/394cffc8da8c4a5cbcfefb9cda79db122011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21283518/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>With the huge amount of uncharacterized protein sequences generated in the post-genomic age, it is highly desirable to develop effective computational methods for quickly and accurately predicting their functions. The information thus obtained would be very useful for both basic research and drug development in a timely manner.<h4>Methodology/principal findings</h4>Although many efforts have been made in this regard, most of them were based on either sequence similarity or protein-protein interaction (PPI) information. However, the former often fails to work if a query protein has no or very little sequence similarity to any function-known proteins, while the latter had similar problem if the relevant PPI information is not available. In view of this, a new approach is proposed by hybridizing the PPI information and the biochemical/physicochemical features of protein sequences. The overall first-order success rates by the new predictor for the functions of mouse proteins on training set and test set were 69.1% and 70.2%, respectively, and the success rate covered by the results of the top-4 order from a total of 24 orders was 65.2%.<h4>Conclusions/significance</h4>The results indicate that the new approach is quite promising that may open a new avenue or direction for addressing the difficult and complicated problem.Lele HuTao HuangXiaohe ShiWen-Cong LuYu-Dong CaiKuo-Chen ChouPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 1, p e14556 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lele Hu Tao Huang Xiaohe Shi Wen-Cong Lu Yu-Dong Cai Kuo-Chen Chou Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties.
description	<h4>Background</h4>With the huge amount of uncharacterized protein sequences generated in the post-genomic age, it is highly desirable to develop effective computational methods for quickly and accurately predicting their functions. The information thus obtained would be very useful for both basic research and drug development in a timely manner.<h4>Methodology/principal findings</h4>Although many efforts have been made in this regard, most of them were based on either sequence similarity or protein-protein interaction (PPI) information. However, the former often fails to work if a query protein has no or very little sequence similarity to any function-known proteins, while the latter had similar problem if the relevant PPI information is not available. In view of this, a new approach is proposed by hybridizing the PPI information and the biochemical/physicochemical features of protein sequences. The overall first-order success rates by the new predictor for the functions of mouse proteins on training set and test set were 69.1% and 70.2%, respectively, and the success rate covered by the results of the top-4 order from a total of 24 orders was 65.2%.<h4>Conclusions/significance</h4>The results indicate that the new approach is quite promising that may open a new avenue or direction for addressing the difficult and complicated problem.
format	article
author	Lele Hu Tao Huang Xiaohe Shi Wen-Cong Lu Yu-Dong Cai Kuo-Chen Chou
author_facet	Lele Hu Tao Huang Xiaohe Shi Wen-Cong Lu Yu-Dong Cai Kuo-Chen Chou
author_sort	Lele Hu
title	Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties.
title_short	Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties.
title_full	Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties.
title_fullStr	Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties.
title_full_unstemmed	Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties.
title_sort	predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/394cffc8da8c4a5cbcfefb9cda79db12
work_keys_str_mv	AT lelehu predictingfunctionsofproteinsinmousebasedonweightedproteinproteininteractionnetworkandproteinhybridproperties AT taohuang predictingfunctionsofproteinsinmousebasedonweightedproteinproteininteractionnetworkandproteinhybridproperties AT xiaoheshi predictingfunctionsofproteinsinmousebasedonweightedproteinproteininteractionnetworkandproteinhybridproperties AT wenconglu predictingfunctionsofproteinsinmousebasedonweightedproteinproteininteractionnetworkandproteinhybridproperties AT yudongcai predictingfunctionsofproteinsinmousebasedonweightedproteinproteininteractionnetworkandproteinhybridproperties AT kuochenchou predictingfunctionsofproteinsinmousebasedonweightedproteinproteininteractionnetworkandproteinhybridproperties
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Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties.

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