Protein Function Prediction Using Function Associations in Protein–Protein Interaction Network
In recent years, the rapid development of high-throughput technology has led to huge amounts of protein-protein interaction (PPI) data and unannotated protein sequences. Many approaches for protein function prediction have been developed which use PPI networks information. Traditional methods usuall...
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doaj-0865e632d4ae48b6834da6fbfee9388c2021-03-29T20:48:48ZengIEEEIEEE Access2169-35362018-01-016308923090210.1109/ACCESS.2018.28064788292822Protein Function Prediction Using Function Associations in Protein–Protein Interaction NetworkPingping Sun0Xian Tan1https://orcid.org/0000-0002-8911-4703Sijia Guo2Jingbo Zhang3Bojian Sun4Ning Du5Han Wang6Hui Sun7School of Information and Technology, Northeast Normal University, Changchun, ChinaSchool of Information and Technology, Northeast Normal University, Changchun, ChinaSchool of Information and Technology, Northeast Normal University, Changchun, ChinaSchool of Information and Technology, Northeast Normal University, Changchun, ChinaSchool of Information and Technology, Northeast Normal University, Changchun, ChinaSchool of Mathematics and Statistics, Northeast Normal University, Changchun, ChinaSchool of Information and Technology, Northeast Normal University, Changchun, ChinaCollege of Humanities and Sciences of Northeast Normal University, Changchun, ChinaIn recent years, the rapid development of high-throughput technology has led to huge amounts of protein-protein interaction (PPI) data and unannotated protein sequences. Many approaches for protein function prediction have been developed which use PPI networks information. Traditional methods usually use the dependencies among interacting proteins for each same function only. However, the functions which are barely linked with the same function are more difficult to predict. In multi-label settings, the dependencies among related instances with multiple labels are more complex; rationally using these associations can make up for the shortcomings of traditional methods. In this paper, an iterative algorithm is applied to predict protein function based on the new network. The proposed method is able to capture the dependencies among functions based on proteins and interactions for protein function prediction. The test results show that the algorithm performs better than most of existing network based PPI algorithms; adding sequence similarity edges and spread function information can really improve the prediction performance. In addition, the dependencies among functions based on proteins and interactions can be effectively applied to the prediction of protein function.https://ieeexplore.ieee.org/document/8292822/Protein function predictionprotein–protein interaction networkfunction dependency |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pingping Sun Xian Tan Sijia Guo Jingbo Zhang Bojian Sun Ning Du Han Wang Hui Sun |
spellingShingle |
Pingping Sun Xian Tan Sijia Guo Jingbo Zhang Bojian Sun Ning Du Han Wang Hui Sun Protein Function Prediction Using Function Associations in Protein–Protein Interaction Network IEEE Access Protein function prediction protein–protein interaction network function dependency |
author_facet |
Pingping Sun Xian Tan Sijia Guo Jingbo Zhang Bojian Sun Ning Du Han Wang Hui Sun |
author_sort |
Pingping Sun |
title |
Protein Function Prediction Using Function Associations in Protein–Protein Interaction Network |
title_short |
Protein Function Prediction Using Function Associations in Protein–Protein Interaction Network |
title_full |
Protein Function Prediction Using Function Associations in Protein–Protein Interaction Network |
title_fullStr |
Protein Function Prediction Using Function Associations in Protein–Protein Interaction Network |
title_full_unstemmed |
Protein Function Prediction Using Function Associations in Protein–Protein Interaction Network |
title_sort |
protein function prediction using function associations in protein–protein interaction network |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2018-01-01 |
description |
In recent years, the rapid development of high-throughput technology has led to huge amounts of protein-protein interaction (PPI) data and unannotated protein sequences. Many approaches for protein function prediction have been developed which use PPI networks information. Traditional methods usually use the dependencies among interacting proteins for each same function only. However, the functions which are barely linked with the same function are more difficult to predict. In multi-label settings, the dependencies among related instances with multiple labels are more complex; rationally using these associations can make up for the shortcomings of traditional methods. In this paper, an iterative algorithm is applied to predict protein function based on the new network. The proposed method is able to capture the dependencies among functions based on proteins and interactions for protein function prediction. The test results show that the algorithm performs better than most of existing network based PPI algorithms; adding sequence similarity edges and spread function information can really improve the prediction performance. In addition, the dependencies among functions based on proteins and interactions can be effectively applied to the prediction of protein function. |
topic |
Protein function prediction protein–protein interaction network function dependency |
url |
https://ieeexplore.ieee.org/document/8292822/ |
work_keys_str_mv |
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