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The Bipartite Network Projection-Recommended Algorithm for Predicting Long Non-coding RNA-Protein Interactions.

Abstract：

:With the development of science and biotechnology, many evidences show that ncRNAs play an important role in the development of important biological processes, especially in chromatin modification, cell differentiation and proliferation, RNA progressing, human diseases, etc. Moreover, lncRNAs account for the majority of ncRNAs, and the functions of lncRNAs are expressed by the related RNA-binding proteins. It is well known that the experimental verification of lncRNA-protein relationships is a waste of time and expensive. So many time-saving and inexpensive computational methods are proposed to uncover potential lncRNA-protein interactions. In this work, we propose a novel computational method to predict the potential lncRNA-protein interactions with the bipartite network projection recommended algorithm (LPI-BNPRA). Our approach is a semi-supervised method based on the lncRNA similarity matrix, protein similarity matrix, and lncRNA-protein interaction matrix. Compared with three previous methods under the leave-one-out cross-validation, our model has a more high-confidence result with the AUC value of 0.8754 and the AUPR value of 0.6283. We also do case studies by the Mus musculus dataset to further reflect the reliability of our approach. This suggests that LPI-BNPRA will be a reliable computational method to uncover lncRNA-protein interactions in biomedical research.

journal_name

Mol Ther Nucleic Acids

journal_title

Molecular therapy. Nucleic acids

authors

Zhao Q,Yu H,Ming Z,Hu H,Ren G,Liu H

doi

10.1016/j.omtn.2018.09.020

subject

Has Abstract

pub_date

2018-12-07 00:00:00

pages

464-471

issn

2162-2531

pii

S2162-2531(18)30264-6

journal_volume

13

pub_type

杂志文章

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