Construction of ontology augmented networks for protein complex prediction.

Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existi...

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Autores principales:	Yijia Zhang, Hongfei Lin, Zhihao Yang, Jian Wang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e30f0c186830448ca5c1af8c54005264
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id	oai:doaj.org-article:e30f0c186830448ca5c1af8c54005264
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spelling	oai:doaj.org-article:e30f0c186830448ca5c1af8c540052642021-11-18T07:47:15ZConstruction of ontology augmented networks for protein complex prediction.1932-620310.1371/journal.pone.0062077https://doaj.org/article/e30f0c186830448ca5c1af8c540052642013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23650509/?tool=EBIhttps://doaj.org/toc/1932-6203Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.Yijia ZhangHongfei LinZhihao YangJian WangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 5, p e62077 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yijia Zhang Hongfei Lin Zhihao Yang Jian Wang Construction of ontology augmented networks for protein complex prediction.
description	Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.
format	article
author	Yijia Zhang Hongfei Lin Zhihao Yang Jian Wang
author_facet	Yijia Zhang Hongfei Lin Zhihao Yang Jian Wang
author_sort	Yijia Zhang
title	Construction of ontology augmented networks for protein complex prediction.
title_short	Construction of ontology augmented networks for protein complex prediction.
title_full	Construction of ontology augmented networks for protein complex prediction.
title_fullStr	Construction of ontology augmented networks for protein complex prediction.
title_full_unstemmed	Construction of ontology augmented networks for protein complex prediction.
title_sort	construction of ontology augmented networks for protein complex prediction.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/e30f0c186830448ca5c1af8c54005264
work_keys_str_mv	AT yijiazhang constructionofontologyaugmentednetworksforproteincomplexprediction AT hongfeilin constructionofontologyaugmentednetworksforproteincomplexprediction AT zhihaoyang constructionofontologyaugmentednetworksforproteincomplexprediction AT jianwang constructionofontologyaugmentednetworksforproteincomplexprediction
_version_	1718423006330486784

Construction of ontology augmented networks for protein complex prediction.

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