PMID- 30388620
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210109
IS  - 2162-2531 (Print)
IS  - 2162-2531 (Electronic)
IS  - 2162-2531 (Linking)
VI  - 13
DP  - 2018 Dec 7
TI  - The Bipartite Network Projection-Recommended Algorithm for Predicting Long 
      Non-coding RNA-Protein Interactions.
PG  - 464-471
LID - S2162-2531(18)30264-6 [pii]
LID - 10.1016/j.omtn.2018.09.020 [doi]
AB  - 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.
CI  - Copyright (c) 2018 The Authors. Published by Elsevier Inc. All rights reserved.
FAU - Zhao, Qi
AU  - Zhao Q
AD  - School of Mathematics, Liaoning University, Shenyang 110036, China.
FAU - Yu, Haifan
AU  - Yu H
AD  - School of Mathematics, Liaoning University, Shenyang 110036, China.
FAU - Ming, Zhong
AU  - Ming Z
AD  - National Engineering Laboratory for Big Data System Computing Technology, 
      Shenzhen University, Shenzhen 518060, China; College of Computer Science and 
      Software Engineering, Shenzhen University, Shenzhen 518060, China.
FAU - Hu, Huan
AU  - Hu H
AD  - School of Life Science, Liaoning University, Shenyang 110036, China.
FAU - Ren, Guofei
AU  - Ren G
AD  - School of Information, Liaoning University, Shenyang 110036, China.
FAU - Liu, Hongsheng
AU  - Liu H
AD  - School of Life Science, Liaoning University, Shenyang 110036, China; Research 
      Center for Computer Simulating and Information Processing of Bio-Macromolecules 
      of Liaoning Province, Shenyang 110036, China; Engineering Laboratory for 
      Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang 
      110036, China. Electronic address: liuhongsheng@lnu.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20180929
PL  - United States
TA  - Mol Ther Nucleic Acids
JT  - Molecular therapy. Nucleic acids
JID - 101581621
PMC - PMC6205413
OTO - NOTNLM
OT  - lncRNA
OT  - lncRNA-protein interaction prediction
OT  - protein
OT  - recommended algorithm
OT  - semi-supervised method
EDAT- 2018/11/06 06:00
MHDA- 2018/11/06 06:01
PMCR- 2018/09/29
CRDT- 2018/11/03 06:00
PHST- 2018/05/23 00:00 [received]
PHST- 2018/09/25 00:00 [revised]
PHST- 2018/09/25 00:00 [accepted]
PHST- 2018/11/06 06:00 [pubmed]
PHST- 2018/11/06 06:01 [medline]
PHST- 2018/11/03 06:00 [entrez]
PHST- 2018/09/29 00:00 [pmc-release]
AID - S2162-2531(18)30264-6 [pii]
AID - 10.1016/j.omtn.2018.09.020 [doi]
PST - ppublish
SO  - Mol Ther Nucleic Acids. 2018 Dec 7;13:464-471. doi: 10.1016/j.omtn.2018.09.020. 
      Epub 2018 Sep 29.