A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions
As one large class of non-coding RNAs (ncRNAs), long ncRNAs (lncRNAs) have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. S...
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doaj-0962aa0a674b4d9d9c1914ea93e525fb2020-11-24T22:44:54ZengElsevierGenomics, Proteomics & Bioinformatics1672-02292016-02-01141627110.1016/j.gpb.2016.01.004A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein InteractionsMengqu Ge0Ao Li1Minghui Wang2School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, ChinaSchool of Information Science and Technology, University of Science and Technology of China, Hefei 230027, ChinaSchool of Information Science and Technology, University of Science and Technology of China, Hefei 230027, ChinaAs one large class of non-coding RNAs (ncRNAs), long ncRNAs (lncRNAs) have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA–protein bipartite network inference (LPBNI). LPBNI aims to identify potential lncRNA–interacting proteins, by making full use of the known lncRNA–protein interactions. Leave-one-out cross validation (LOOCV) test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR) and protein-based collaborative filtering (ProCF). Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA–interacting proteins.http://www.sciencedirect.com/science/article/pii/S1672022916000413lncRNAProteinInteractionBipartite networkPropagation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mengqu Ge Ao Li Minghui Wang |
spellingShingle |
Mengqu Ge Ao Li Minghui Wang A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions Genomics, Proteomics & Bioinformatics lncRNA Protein Interaction Bipartite network Propagation |
author_facet |
Mengqu Ge Ao Li Minghui Wang |
author_sort |
Mengqu Ge |
title |
A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions |
title_short |
A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions |
title_full |
A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions |
title_fullStr |
A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions |
title_full_unstemmed |
A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions |
title_sort |
bipartite network-based method for prediction of long non-coding rna–protein interactions |
publisher |
Elsevier |
series |
Genomics, Proteomics & Bioinformatics |
issn |
1672-0229 |
publishDate |
2016-02-01 |
description |
As one large class of non-coding RNAs (ncRNAs), long ncRNAs (lncRNAs) have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA–protein bipartite network inference (LPBNI). LPBNI aims to identify potential lncRNA–interacting proteins, by making full use of the known lncRNA–protein interactions. Leave-one-out cross validation (LOOCV) test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR) and protein-based collaborative filtering (ProCF). Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA–interacting proteins. |
topic |
lncRNA Protein Interaction Bipartite network Propagation |
url |
http://www.sciencedirect.com/science/article/pii/S1672022916000413 |
work_keys_str_mv |
AT mengquge abipartitenetworkbasedmethodforpredictionoflongnoncodingrnaproteininteractions AT aoli abipartitenetworkbasedmethodforpredictionoflongnoncodingrnaproteininteractions AT minghuiwang abipartitenetworkbasedmethodforpredictionoflongnoncodingrnaproteininteractions AT mengquge bipartitenetworkbasedmethodforpredictionoflongnoncodingrnaproteininteractions AT aoli bipartitenetworkbasedmethodforpredictionoflongnoncodingrnaproteininteractions AT minghuiwang bipartitenetworkbasedmethodforpredictionoflongnoncodingrnaproteininteractions |
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1725689942472916992 |