Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features

Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Lei Chen, ZhanDong Li, Tao Zeng, Yu-Hang Zhang, ShiQi Zhang, Tao Huang, Yu-Dong Cai
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	protein subcellular location protein-protein interaction network GO enrichment KEGG enrichment feature selection classification algorithm Genetics QH426-470
Acceso en línea:	https://doaj.org/article/2d9460bfb6d944b497abaa0ee0ab677f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2d9460bfb6d944b497abaa0ee0ab677f
record_format	dspace
spelling	oai:doaj.org-article:2d9460bfb6d944b497abaa0ee0ab677f2021-11-05T15:12:59ZPredicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features1664-802110.3389/fgene.2021.783128https://doaj.org/article/2d9460bfb6d944b497abaa0ee0ab677f2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.783128/fullhttps://doaj.org/toc/1664-8021Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein–protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein–protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.Lei ChenLei ChenZhanDong LiTao ZengYu-Hang ZhangShiQi ZhangTao HuangTao HuangYu-Dong CaiFrontiers Media S.A.articleprotein subcellular locationprotein-protein interaction networkGO enrichmentKEGG enrichmentfeature selectionclassification algorithmGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	protein subcellular location protein-protein interaction network GO enrichment KEGG enrichment feature selection classification algorithm Genetics QH426-470
spellingShingle	protein subcellular location protein-protein interaction network GO enrichment KEGG enrichment feature selection classification algorithm Genetics QH426-470 Lei Chen Lei Chen ZhanDong Li Tao Zeng Yu-Hang Zhang ShiQi Zhang Tao Huang Tao Huang Yu-Dong Cai Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features
description	Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein–protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein–protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.
format	article
author	Lei Chen Lei Chen ZhanDong Li Tao Zeng Yu-Hang Zhang ShiQi Zhang Tao Huang Tao Huang Yu-Dong Cai
author_facet	Lei Chen Lei Chen ZhanDong Li Tao Zeng Yu-Hang Zhang ShiQi Zhang Tao Huang Tao Huang Yu-Dong Cai
author_sort	Lei Chen
title	Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features
title_short	Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features
title_full	Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features
title_fullStr	Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features
title_full_unstemmed	Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features
title_sort	predicting human protein subcellular locations by using a combination of network and function features
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/2d9460bfb6d944b497abaa0ee0ab677f
work_keys_str_mv	AT leichen predictinghumanproteinsubcellularlocationsbyusingacombinationofnetworkandfunctionfeatures AT leichen predictinghumanproteinsubcellularlocationsbyusingacombinationofnetworkandfunctionfeatures AT zhandongli predictinghumanproteinsubcellularlocationsbyusingacombinationofnetworkandfunctionfeatures AT taozeng predictinghumanproteinsubcellularlocationsbyusingacombinationofnetworkandfunctionfeatures AT yuhangzhang predictinghumanproteinsubcellularlocationsbyusingacombinationofnetworkandfunctionfeatures AT shiqizhang predictinghumanproteinsubcellularlocationsbyusingacombinationofnetworkandfunctionfeatures AT taohuang predictinghumanproteinsubcellularlocationsbyusingacombinationofnetworkandfunctionfeatures AT taohuang predictinghumanproteinsubcellularlocationsbyusingacombinationofnetworkandfunctionfeatures AT yudongcai predictinghumanproteinsubcellularlocationsbyusingacombinationofnetworkandfunctionfeatures
_version_	1718444151537664000

Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features

Ejemplares similares