Login Register Cart 0
Generic placeholder image

Protein & Peptide Letters

Editor-in-Chief

ISSN (Print): 0929-8665
ISSN (Online): 1875-5305

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review

Author(s): Lin Zhong, Zhong Ming, Guobo Xie, Chunlong Fan* and Xue Piao*

Volume 27, Issue 5, 2020

Page: [385 - 391] Pages: 7

DOI: 10.2174/0929866526666191025104043

Price: $65

Abstract

In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

Keywords: lncRNA, protein, interactions prediction, computational prediction models, semi-supervised learning, biological processes.

« Previous Next »
Graphical Abstract

[1]
Guttman, M.; Amit, I.; Garber, M.; French, C.; Lin, M.F.; Feldser, D.; Huarte, M.; Zuk, O.; Carey, B.W.; Cassady, J.P.; Cabili, M.N.; Jaenisch, R.; Mikkelsen, T.S.; Jacks, T.; Hacohen, N.; Bernstein, B.E.; Kellis, M.; Regev, A.; Rinn, J.L.; Lander, E.S. Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals. Nature, 2009, 458(7235), 223-227.
[http://dx.doi.org/10.1038/nature07672] [PMID: 19182780]
[2]
Wilusz, J.E.; Sunwoo, H.; Spector, D.L. Long noncoding RNAs: functional surprises from the RNA world. Genes Dev., 2009, 23(13), 1494-1504.
[http://dx.doi.org/10.1101/gad.1800909] [PMID: 19571179]
[3]
Mercer, T.R.; Dinger, M.E.; Mattick, J.S. Long non-coding RNAs: insights into functions. Nat. Rev. Genet., 2009, 10(3), 155-159.
[http://dx.doi.org/10.1038/nrg2521] [PMID: 19188922]
[4]
Wapinski, O.; Chang, H.Y. Long noncoding RNAs and human disease. Trends Cell Biol., 2011, 21(6), 354-361.
[http://dx.doi.org/10.1016/j.tcb.2011.04.001] [PMID: 21550244]
[5]
Chen, X.; Yan, C.C.; Zhang, X.; You, Z.H. Long non-coding RNAs and complex diseases: from experimental results to computational models. Brief. Bioinform., 2017, 18(4), 558-576.
[PMID: 27345524]
[6]
Yang, F.; Zhang, H.; Mei, Y.; Wu, M. Reciprocal regulation of HIF-1α and lincRNA-p21 modulates the Warburg effect. Mol. Cell, 2014, 53(1), 88-100.
[http://dx.doi.org/10.1016/j.molcel.2013.11.004] [PMID: 24316222]
[7]
Li, Z.W.; You, Z.H.; Chen, X.; Gui, J.; Nie, R. Highly accurate prediction of protein-protein interactions via incorporating evolutionary information and physicochemical characteristics. Int. J. Mol. Sci., 2016, 17(9), 1396.
[http://dx.doi.org/10.3390/ijms17091396] [PMID: 27571061]
[8]
Huang, Y.A.; You, Z.H.; Chen, X.; Chan, K.; Luo, X. Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding. BMC Bioinformatics, 2016, 17(1), 184.
[http://dx.doi.org/10.1186/s12859-016-1035-4] [PMID: 27112932]
[9]
Chen, X.; Huang, Y.A.; You, Z.H.; Yan, G.Y.; Wang, X.S. A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases. Bioinformatics, 2017, 33(5), 733-739.
[PMID: 28025197]
[10]
Chen, X.; Huang, L. LRSSLMDA: Laplacian regularized sparse subspace learning for MiRNA-disease association prediction. PLOS Comput. Biol., 2017, 13(12), e1005912
[http://dx.doi.org/10.1371/journal.pcbi.1005912] [PMID: 29253885]
[11]
Chen, X.; Xie, D.; Zhao, Q.; You, Z.H. MicroRNAs and complex diseases: from experimental results to computational models. Brief. Bioinform., 2019, 20(2), 515-539.
[http://dx.doi.org/10.1093/bib/bbx130] [PMID: 29045685]
[12]
Chen, X.; Yan, C.C.; Zhang, X.; Zhang, X.; Dai, F.; Yin, J.; Zhang, Y. Drug-target interaction prediction: databases, web servers and computational models. Brief. Bioinform., 2016, 17(4), 696-712.
[http://dx.doi.org/10.1093/bib/bbv066] [PMID: 26283676]
[13]
Chen, X.; Ren, B.; Chen, M.; Wang, Q.; Zhang, L.; Yan, G. NLLSS: Predicting synergistic drug combinations based on semi-supervised learning. PLOS Comput. Biol., 2016, 12(7), e1004975
[http://dx.doi.org/10.1371/journal.pcbi.1004975] [PMID: 27415801]
[14]
Chen, X.; Wang, L.; Qu, J.; Guan, N.N.; Li, J.Q. Predicting miRNA-disease association based on inductive matrix completion. Bioinformatics, 2018, 34(24), 4256-4265.
[http://dx.doi.org/10.1093/bioinformatics/bty503] [PMID: 29939227]
[15]
Chen, X.; Yin, J.; Qu, J.; Huang, L. MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction. PLOS Comput. Biol., 2018, 14(8), e1006418
[http://dx.doi.org/10.1371/journal.pcbi.1006418] [PMID: 30142158]
[16]
Chen, X.; Huang, L.; Xie, D.; Zhao, Q. EGBMMDA: Extreme gradient boosting machine for MiRNA-disease association prediction. Cell Death Dis., 2018, 9(1), 3.
[http://dx.doi.org/10.1038/s41419-017-0003-x] [PMID: 29305594]
[17]
You, Z.H.; Huang, Z.A.; Zhu, Z.; Yan, G.Y.; Li, Z.W.; Wen, Z.; Chen, X. PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction. PLOS Comput. Biol., 2017, 13(3), e1005455
[http://dx.doi.org/10.1371/journal.pcbi.1005455] [PMID: 28339468]
[18]
Chen, X.; Yan, G.Y. Novel human lncRNA-disease association inference based on lncRNA expression profiles. Bioinformatics, 2013, 29(20), 2617-2624.
[http://dx.doi.org/10.1093/bioinformatics/btt426] [PMID: 24002109]
[19]
Hu, H.; Zhu, C.; Ai, H.; Zhang, L.; Zhao, J.; Zhao, Q.; Liu, H. LPI-ETSLP: lncRNA-protein interaction prediction using eigenvalue transformation-based semi-supervised link prediction. Mol. Biosyst., 2017, 13(9), 1781-1787.
[http://dx.doi.org/10.1039/C7MB00290D] [PMID: 28702594]
[20]
Zhao, Q.; Zhang, Y.; Hu, H.; Ren, G.; Zhang, W.; Liu, H. IRWNRLPI: Integrating random walk and neighborhood regularized logistic matrix factorization for lncRNA-protein interaction prediction. Front. Genet., 2018, 9, 239.
[http://dx.doi.org/10.3389/fgene.2018.00239] [PMID: 30023002]
[21]
Zhao, Q.; Liang, D.; Hu, H.; Ren, G.; Liu, H. RWLPAP: Random walk for lncRNA-protein associations prediction. Protein Pept. Lett., 2018, 25(9), 830-837.
[http://dx.doi.org/10.2174/0929866525666180905104904] [PMID: 30182833]
[22]
Zhao, Q.; Yu, H.; Ming, Z.; Hu, H.; Ren, G.; Liu, H. The bipartite network projection-recommended algorithm for predicting long non-coding RNA-protein interactions. Mol. Ther. Nucleic Acids, 2018, 13, 464-471.
[http://dx.doi.org/10.1016/j.omtn.2018.09.020] [PMID: 30388620]
[23]
Hu, H.; Zhang, L.; Ai, H.; Zhang, H.; Fan, Y.; Zhao, Q.; Liu, H. HLPI-Ensemble: Prediction of human lncRNA-protein interactions based on ensemble strategy. RNA Biol., 2018, 15(6), 797-806.
[http://dx.doi.org/10.1080/15476286.2018.1457935] [PMID: 29583068]
[24]
Zhu, X.; Goldberg, A.B. Introduction to semi-supervised learning. Semi-supervised Learning, 2009, 3(1), 130.
[25]
Yuan, J.; Wu, W.; Xie, C.; Zhao, G.; Zhao, Y.; Chen, R. NPInter v2.0: an updated database of ncRNA interactions. Nucleic Acids Res., 2014, 42(Database issue), D104-D108.
[http://dx.doi.org/10.1093/nar/gkt1057] [PMID: 24217916]
[26]
Zhao, Y.; Yuan, J.; Chen, R. NONCODEv4: Annotation of noncoding RNAs with emphasis on long noncoding RNAs. Methods Mol. Biol., 2016, 1402, 243-254.
[http://dx.doi.org/10.1007/978-1-4939-3378-5_19] [PMID: 26721496]
[27]
Pundir, S.; Martin, M. J.; O'Donovan, C. The UniProt Consortium 2016. UniProt tools. Curr. Protoc. Bioinform., 2016, 53, 1.29.1-1.29.15.
[http://dx.doi.org/10.1002/0471250953.bi0129s53]
[28]
Liu, H.; Ren, G.; Hu, H.; Zhang, L.; Ai, H.; Zhang, W.; Zhao, Q. LPI-NRLMF: lncRNA-protein interaction prediction by neighborhood regularized logistic matrix factorization. Oncotarget, 2017, 8(61), 103975-103984.
[http://dx.doi.org/10.18632/oncotarget.21934] [PMID: 29262614]
[29]
van Laarhoven, T.; Nabuurs, S.B.; Marchiori, E. Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics, 2011, 27(21), 3036-3043.
[http://dx.doi.org/10.1093/bioinformatics/btr500] [PMID: 21893517]
[30]
Zhang, W.; Qu, Q.; Zhang, Y.; Wei, W.; Wen, Z.; Qu, Q.; Zhang, Y.; Wei, W. The linear neighborhood propagation method for predicting long non-coding RNA-protein interactions. Neurocomputing, 2017, 273, 526-534.
[http://dx.doi.org/10.1016/j.neucom.2017.07.065]
[31]
Liu, Y.; Wu, M.; Miao, C.; Zhao, P.; Li, X.L. Neighborhood regularized logistic matrix factorization for drug-target interaction prediction. PLOS Comput. Biol., 2016, 12(2), e1004760
[http://dx.doi.org/10.1371/journal.pcbi.1004760] [PMID: 26872142]
[32]
Chen, X.; Xie, D.; Wang, L.; Zhao, Q.; You, Z.H.; Liu, H. BNPMDA: Bipartite network projection for MiRNA-disease association prediction. Bioinformatics, 2018, 34(18), 3178-3186.
[http://dx.doi.org/10.1093/bioinformatics/bty333] [PMID: 29701758]

Rights & Permissions Print Cite
Article Metrics
28
2
© 2024 Bentham Science Publishers | Privacy Policy