Predicting essential proteins by integrating orthology, gene expressions, and PPI networks.

Identifying essential proteins is very important for understanding the minimal requirements of cellular life and finding human disease genes as well as potential drug targets. Experimental methods for identifying essential proteins are often costly, time-consuming, and laborious. Many computational...

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Main Authors: Xue Zhang, Wangxin Xiao, Xihao Hu
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2018-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC5892885?pdf=render
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author Xue Zhang
Wangxin Xiao
Xihao Hu
author_facet Xue Zhang
Wangxin Xiao
Xihao Hu
author_sort Xue Zhang
collection DOAJ
description Identifying essential proteins is very important for understanding the minimal requirements of cellular life and finding human disease genes as well as potential drug targets. Experimental methods for identifying essential proteins are often costly, time-consuming, and laborious. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction networks (PINs). However, most of these methods have limited prediction accuracy due to the noisy and incomplete natures of PINs and the fact that protein essentiality may relate to multiple biological factors. In this work, we proposed a new centrality measure, OGN, by integrating orthologous information, gene expressions, and PINs together. OGN determines a protein's essentiality by capturing its co-clustering and co-expression properties, as well as its conservation in the evolution process. The performance of OGN was tested on the species of Saccharomyces cerevisiae. Compared with several published centrality measures, OGN achieves higher prediction accuracy in both working alone and ensemble.
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spelling doaj.art-748d540819e340dd8307f7b50d2449452022-12-21T18:18:21ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01134e019541010.1371/journal.pone.0195410Predicting essential proteins by integrating orthology, gene expressions, and PPI networks.Xue ZhangWangxin XiaoXihao HuIdentifying essential proteins is very important for understanding the minimal requirements of cellular life and finding human disease genes as well as potential drug targets. Experimental methods for identifying essential proteins are often costly, time-consuming, and laborious. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction networks (PINs). However, most of these methods have limited prediction accuracy due to the noisy and incomplete natures of PINs and the fact that protein essentiality may relate to multiple biological factors. In this work, we proposed a new centrality measure, OGN, by integrating orthologous information, gene expressions, and PINs together. OGN determines a protein's essentiality by capturing its co-clustering and co-expression properties, as well as its conservation in the evolution process. The performance of OGN was tested on the species of Saccharomyces cerevisiae. Compared with several published centrality measures, OGN achieves higher prediction accuracy in both working alone and ensemble.http://europepmc.org/articles/PMC5892885?pdf=render
spellingShingle Xue Zhang
Wangxin Xiao
Xihao Hu
Predicting essential proteins by integrating orthology, gene expressions, and PPI networks.
PLoS ONE
title Predicting essential proteins by integrating orthology, gene expressions, and PPI networks.
title_full Predicting essential proteins by integrating orthology, gene expressions, and PPI networks.
title_fullStr Predicting essential proteins by integrating orthology, gene expressions, and PPI networks.
title_full_unstemmed Predicting essential proteins by integrating orthology, gene expressions, and PPI networks.
title_short Predicting essential proteins by integrating orthology, gene expressions, and PPI networks.
title_sort predicting essential proteins by integrating orthology gene expressions and ppi networks
url http://europepmc.org/articles/PMC5892885?pdf=render
work_keys_str_mv AT xuezhang predictingessentialproteinsbyintegratingorthologygeneexpressionsandppinetworks
AT wangxinxiao predictingessentialproteinsbyintegratingorthologygeneexpressionsandppinetworks
AT xihaohu predictingessentialproteinsbyintegratingorthologygeneexpressionsandppinetworks