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

作     者：Zhao, Qi Yu, Haifan Ming, Zhong Hu, Huan Ren, Guofei Liu, Hongsheng 

作者机构：Liaoning Univ Sch Math Shenyang 110036 Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Liaoning Univ Sch Life Sci Shenyang 110036 Peoples R China Liaoning Univ Sch Informat Shenyang 110036 Peoples R China Res Ctr Comp Simulating & Informat Proc Biomacrom Shenyang 110036 Peoples R China Engn Lab Mol Simulat & Designing Drug Mol Liaonin Shenyang 110036 Peoples R China 

出 版 物：《MOLECULAR THERAPY-NUCLEIC ACIDS》 (Mol. Ther. Nucl. Acids)

年 卷 期：2018年第13卷

页      面：464-471页

核心收录：

基　　金：National Natural Science Foundation of China [31570160, 61772531] Innovation Team Project of Education Department of Liaoning Province [LT2015011] Doctor Startup Foundation from Liaoning Province Important Scientific and Technical Achievements Transformation Project [Z17-5-078] Large-Scale Equipment Shared Services Project [F15165400] Applied Basic Research Project [F16205151] 

主　　题：lncRNA protein lncRNA-protein interaction prediction semi-supervised method recommended algorithm 

摘      要：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.