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      IRWNRLPI: Integrating Random Walk and Neighborhood Regularized Logistic Matrix Factorization for lncRNA-Protein Interaction Prediction

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          Abstract

          Long non-coding RNA (lncRNA) plays an important role in many important biological processes and has attracted widespread attention. Although the precise functions and mechanisms for most lncRNAs are still unknown, we are certain that lncRNAs usually perform their functions by interacting with the corresponding RNA- binding proteins. For example, lncRNA-protein interactions play an important role in post transcriptional gene regulation, such as splicing, translation, signaling, and advances in complex diseases. However, experimental verification of lncRNA-protein interactions prediction is time-consuming and laborious. In this work, we propose a computational method, named IRWNRLPI, to find the potential associations between lncRNAs and proteins. IRWNRLPI integrates two algorithms, random walk and neighborhood regularized logistic matrix factorization, which can optimize a lot more than using an algorithm alone. Moreover, the method is semi-supervised and does not require negative samples. Based on the leave-one-out cross validation, we obtain the AUC of 0.9150 and the AUPR of 0.7138, demonstrating its reliable performance. In addition, by means of case study in the “Mus musculus,” many lncRNA-protein interactions which are predicted by our method can be successfully confirmed by experiments. This suggests that IRWNRLPI will be a useful bioinformatics resource in biomedical research.

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          Most cited references32

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          Support vector machines

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            Long non-coding RNAs and complex diseases: from experimental results to computational models

            Abstract LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA–disease associations and predicting potential human lncRNA–disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.
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              RBPDB: a database of RNA-binding specificities

              The RNA-Binding Protein DataBase (RBPDB) is a collection of experimental observations of RNA-binding sites, both in vitro and in vivo, manually curated from primary literature. To build RBPDB, we performed a literature search for experimental binding data for all RNA-binding proteins (RBPs) with known RNA-binding domains in four metazoan species (human, mouse, fly and worm). In total, RPBDB contains binding data on 272 RBPs, including 71 that have motifs in position weight matrix format, and 36 sets of sequences of in vivo-bound transcripts from immunoprecipitation experiments. The database is accessible by a web interface which allows browsing by domain or by organism, searching and export of records, and bulk data downloads. Users can also use RBPDB to scan sequences for RBP-binding sites. RBPDB is freely available, without registration at http://rbpdb.ccbr.utoronto.ca/.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                04 July 2018
                2018
                : 9
                : 239
                Affiliations
                [1] 1School of Mathematics, Liaoning University , Shenyang, China
                [2] 2Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Liaoning Province , Shenyang, China
                [3] 3School of Life Science, Liaoning University , Shenyang, China
                [4] 4School of Information, Liaoning University , Shenyang, China
                [5] 5School of Computer, Wuhan University , Wuhan, China
                [6] 6Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning , Shenyang, China
                Author notes

                Edited by: Quan Zou, Tianjin University, China

                Reviewed by: Yi Xiong, Shanghai Jiao Tong University, China; Yongqiang Xing, Inner Mongolia University of Science and Technology, China

                *Correspondence: Hongsheng Liu liuhongsheng@ 123456lnu.edu.cn

                This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2018.00239
                6040094
                338babbc-9dd6-4f3f-8e41-589989109f66
                Copyright © 2018 Zhao, Zhang, Hu, Ren, Zhang and Liu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 14 May 2018
                : 15 June 2018
                Page count
                Figures: 3, Tables: 2, Equations: 25, References: 53, Pages: 12, Words: 7443
                Categories
                Genetics
                Original Research

                Genetics
                lncrna,protein,interaction prediction,random walk,neighborhood regularized logistic matrix factorization,integration method

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