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      LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender Algorithm

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          Abstract

          According to the latest research, lncRNAs (long non-coding RNAs) play a broad and important role in various biological processes by interacting with proteins. However, identifying whether proteins interact with a specific lncRNA through biological experimental methods is difficult, costly, and time-consuming. Thus, many bioinformatics computational methods have been proposed to predict lncRNA-protein interactions. In this paper, we proposed a novel approach called Long non-coding RNA-Protein Interaction Prediction based on Improved Bipartite Network Recommender Algorithm (LPI-IBNRA). In the proposed method, we implemented a two-round resource allocation and eliminated the second-order correlations appropriately on the bipartite network. Experimental results illustrate that LPI-IBNRA outperforms five previous methods, with the AUC values of 0.8932 in leave-one-out cross validation (LOOCV) and 0.8819 ± 0.0052 in 10-fold cross validation, respectively. In addition, case studies on four lncRNAs were carried out to show the predictive power of LPI-IBNRA.

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          Most cited references46

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          GAS5, a non-protein-coding RNA, controls apoptosis and is downregulated in breast cancer.

          Effective control of both cell survival and cell proliferation is critical to the prevention of oncogenesis and to successful cancer therapy. Using functional expression cloning, we have identified GAS5 (growth arrest-specific transcript 5) as critical to the control of mammalian apoptosis and cell population growth. GAS5 transcripts are subject to complex post-transcriptional processing and some, but not all, GAS5 transcripts sensitize mammalian cells to apoptosis inducers. We have found that, in some cell lines, GAS5 expression induces growth arrest and apoptosis independently of other stimuli. GAS5 transcript levels were significantly reduced in breast cancer samples relative to adjacent unaffected normal breast epithelial tissues. The GAS5 gene has no significant protein-coding potential but expression encodes small nucleolar RNAs (snoRNAs) in its introns. Taken together with the recent demonstration of tumor suppressor characteristics in the related snoRNA U50, our observations suggest that such snoRNAs form a novel family of genes controlling oncogenesis and sensitivity to therapy in cancer.
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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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              Regulation of transcription by long noncoding RNAs.

              Over the past decade there has been a greater understanding of genomic complexity in eukaryotes ushered in by the immense technological advances in high-throughput sequencing of DNA and its corresponding RNA transcripts. This has resulted in the realization that beyond protein-coding genes, there are a large number of transcripts that do not encode for proteins and, therefore, may perform their function through RNA sequences and/or through secondary and tertiary structural determinants. This review is focused on the latest findings on a class of noncoding RNAs that are relatively large (>200 nucleotides), display nuclear localization, and use different strategies to regulate transcription. These are exciting times for discovering the biological scope and the mechanism of action for these RNA molecules, which have roles in dosage compensation, imprinting, enhancer function, and transcriptional regulation, with a great impact on development and disease.
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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
                18 April 2019
                2019
                : 10
                : 343
                Affiliations
                [1] 1School of Computers, Guangdong University of Technology , Guangzhou, China
                [2] 2Department of Emergency, The First Affiliated Hospital of Sun Yat-sen University , Guangzhou, China
                Author notes

                Edited by: Quan Zou, University of Electronic Science and Technology of China, China

                Reviewed by: Fei Guo, Tianjin University, China; Jia Qu, China University of Mining and Technology, China; Qi Zhao, Liaoning University, China

                *Correspondence: Yuping Sun syp@ 123456gdut.edu.cn

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

                †These authors have contributed equally to this work

                Article
                10.3389/fgene.2019.00343
                6482170
                31057602
                8a94debf-8104-4723-a1e3-7dd5c137f329
                Copyright © 2019 Xie, Wu, Sun, Fan 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
                : 26 January 2019
                : 29 March 2019
                Page count
                Figures: 3, Tables: 2, Equations: 28, References: 66, Pages: 10, Words: 5971
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 618002072
                Funded by: Natural Science Foundation of Guangdong Province 10.13039/501100003453
                Categories
                Genetics
                Original Research

                Genetics
                lncrna,protein,interaction prediction,bipartite network,second-order correlation elimination

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