Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks

Abstract Massive studies have indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes by binding with RNA-related proteins. However, only a few experimentally supported lncRNA-protein associations have been reported. Existing network-based metho...

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Autores principales:	Yun Xiao, Jingpu Zhang, Lei Deng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d14940d4169c4010ba31368437eeea28
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spelling	oai:doaj.org-article:d14940d4169c4010ba31368437eeea282021-12-02T16:06:12ZPrediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks10.1038/s41598-017-03986-12045-2322https://doaj.org/article/d14940d4169c4010ba31368437eeea282017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-03986-1https://doaj.org/toc/2045-2322Abstract Massive studies have indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes by binding with RNA-related proteins. However, only a few experimentally supported lncRNA-protein associations have been reported. Existing network-based methods are typically focused on intrinsic features of lncRNA and protein but ignore the information implicit in the topologies of biological networks associated with lncRNAs. Considering the limitations in previous methods, we propose PLPIHS, an effective computational method for Predicting lncRNA-Protein Interactions using HeteSim Scores. PLPIHS uses the HeteSim measure to calculate the relatedness score for each lncRNA-protein pair in the heterogeneous network, which consists of lncRNA-lncRNA similarity network, lncRNA-protein association network and protein-protein interaction network. An SVM classifier to predict lncRNA-protein interactions is built with the HeteSim scores. The results show that PLPIHS performs significantly better than the existing state-of-the-art approaches and achieves an AUC score of 0.97 in the leave-one-out validation test. We also compare the performances of networks with different connectivity density and find that PLPIHS performs well across all the networks. Furthermore, we use the proposed method to identify the related proteins for lncRNA MALAT1. Highly-ranked proteins are verified by the biological studies and demonstrate the effectiveness of our method.Yun XiaoJingpu ZhangLei DengNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yun Xiao Jingpu Zhang Lei Deng Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks
description	Abstract Massive studies have indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes by binding with RNA-related proteins. However, only a few experimentally supported lncRNA-protein associations have been reported. Existing network-based methods are typically focused on intrinsic features of lncRNA and protein but ignore the information implicit in the topologies of biological networks associated with lncRNAs. Considering the limitations in previous methods, we propose PLPIHS, an effective computational method for Predicting lncRNA-Protein Interactions using HeteSim Scores. PLPIHS uses the HeteSim measure to calculate the relatedness score for each lncRNA-protein pair in the heterogeneous network, which consists of lncRNA-lncRNA similarity network, lncRNA-protein association network and protein-protein interaction network. An SVM classifier to predict lncRNA-protein interactions is built with the HeteSim scores. The results show that PLPIHS performs significantly better than the existing state-of-the-art approaches and achieves an AUC score of 0.97 in the leave-one-out validation test. We also compare the performances of networks with different connectivity density and find that PLPIHS performs well across all the networks. Furthermore, we use the proposed method to identify the related proteins for lncRNA MALAT1. Highly-ranked proteins are verified by the biological studies and demonstrate the effectiveness of our method.
format	article
author	Yun Xiao Jingpu Zhang Lei Deng
author_facet	Yun Xiao Jingpu Zhang Lei Deng
author_sort	Yun Xiao
title	Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks
title_short	Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks
title_full	Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks
title_fullStr	Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks
title_full_unstemmed	Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks
title_sort	prediction of lncrna-protein interactions using hetesim scores based on heterogeneous networks
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/d14940d4169c4010ba31368437eeea28
work_keys_str_mv	AT yunxiao predictionoflncrnaproteininteractionsusinghetesimscoresbasedonheterogeneousnetworks AT jingpuzhang predictionoflncrnaproteininteractionsusinghetesimscoresbasedonheterogeneousnetworks AT leideng predictionoflncrnaproteininteractionsusinghetesimscoresbasedonheterogeneousnetworks
_version_	1718385117036019712

Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks

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