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Current Genomics

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ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

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Review Article

Applications of Machine Learning in miRNA Discovery and Target Prediction

Author(s): Alisha Parveen, Syed H. Mustafa, Pankaj Yadav and Abhishek Kumar*

Volume 20, Issue 8, 2019

Page: [537 - 544] Pages: 8

DOI: 10.2174/1389202921666200106111813

Price: $65

Abstract

MicroRNA (miRNA) is a small non-coding molecule that is involved in gene regulation and RNA silencing by complementary on their targets. Experimental methods for target prediction can be time-consuming and expensive. Thus, the application of the computational approach is implicated to enlighten these complications with experimental studies. However, there is still a need for an optimized approach in miRNA biology. Therefore, machine learning (ML) would initiate a new era of research in miRNA biology towards potential diseases biomarker. In this article, we described the application of ML approaches in miRNA discovery and target prediction with functions and future prospective. The implementation of a new era of computational methodologies in this direction would initiate further advanced levels of discoveries in miRNA.

Keywords: microRNA, machine learning, target prediction, gene expression, feature generation, feature selection.

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[1]
Kaeuferle, T.; Bartel, S.; Dehmel, S.; Krauss-Etschmann, S. MicroRNA methodology: advances in miRNA technologies. Methods Mol. Biol., 2014, 1169, 121-130.
[http://dx.doi.org/10.1007/978-1-4939-0882-0_12] [PMID: 24957235]
[2]
Felekkis, K.; Touvana, E.; Stefanou, Ch.; Deltas, C. MicroRNAs: A newly described class of encoded molecules that play a role in health and disease. Hippokratia, 2010, 14(4), 236-240.
[PMID: 21311629]
[3]
Lee, R.C.; Feinbaum, R.L.; Ambros, V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell, 1993, 75(5), 843-854.
[http://dx.doi.org/10.1016/0092-8674(93)90529-Y] [PMID: 8252621]
[4]
Bhaskaran, M.; Mohan, M. MicroRNAs: History, biogenesis, and their evolving role in animal development and disease. Vet. Pathol., 2014, 51(4), 759-774.
[http://dx.doi.org/10.1177/0300985813502820] [PMID: 24045890]
[5]
Shi, Z.; Hayes, G.; Ruvkun, G. Dual regulation of the lin-14 target mRNA by the lin-4 miRNA. PLoS One, 2013, 8(9)e75475
[http://dx.doi.org/10.1371/journal.pone.0075475] [PMID: 24058689]
[6]
Obernosterer, G.; Leuschner, P.J.; Alenius, M.; Martinez, J. Post-transcriptional regulation of microRNA expression. RNA, 2006, 12(7), 1161-1167.
[http://dx.doi.org/10.1261/rna.2322506] [PMID: 16738409]
[7]
Ha, M.; Kim, V.N. Regulation of microRNA biogenesis. Nat. Rev. Mol. Cell Biol., 2014, 15(8), 509-524.
[http://dx.doi.org/10.1038/nrm3838] [PMID: 25027649]
[8]
Parveen, A.; Gretz, N.; Dweep, H. Obtaining miRNA-target interaction information from miRWalk2.0. Curr. Protoc. Bioinformatics,, 2016, 55(1), 12.15.1-12.15.27..
[9]
Allmer, J.; Yousef, M. Computational miRNomics. J. Integr. Bioinform., 2016, 13(5), 1-2.
[http://dx.doi.org/10.1515/jib-2016-302] [PMID: 29216003]
[10]
Kozomara, A.; Griffiths-Jones, S. miRBase: Annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res., 2014, 42(Database issue), D68-D73.
[http://dx.doi.org/10.1093/nar/gkt1181] [PMID: 24275495]
[11]
Lorenz, R.; Bernhart, S.H.; Höner Zu Siederdissen, C.; Tafer, H.; Flamm, C.; Stadler, P.F.; Hofacker, I.L. ViennaRNA Package 2.0. Algorithms Mol. Biol., 2011, 6, 26.
[http://dx.doi.org/10.1186/1748-7188-6-26] [PMID: 22115189]
[12]
Medved, D.; Nugues, P.; Nilsson, J. Predicting the outcome for patients in a heart transplantation queue using deep learning. Conf. Proc. IEEE Eng. Med. Biol. Soc., 2017, 2017, 74-77.
[http://dx.doi.org/10.1109/EMBC.2017.8036766] [PMID: 29059814]
[13]
Chen, Q.; Cao, F. Distributed support vector machine in master-slave mode. Neural Netw., 2018, 101, 94-100.
[http://dx.doi.org/10.1016/j.neunet.2018.02.006] [PMID: 29494875]
[14]
Sebastian, B.; Aggrey, S.E. pecificity and sensitivity of PROMIR, ERPIN and MIR-ABELA in predicting pre-microRNAs in the chicken genome. In: Silico Biol. (Gedrukt); , 2008; 8, pp. (5-6)377-381.
[PMID: 19374126]
[15]
Xue, C.; Li, F.; He, T.; Liu, G.P.; Li, Y.; Zhang, X. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics, 2005, 6, 310.
[http://dx.doi.org/10.1186/1471-2105-6-310] [PMID: 16381612]
[16]
Lindow, M.; Gorodkin, J. Principles and limitations of computational microRNA gene and target finding. DNA Cell Biol., 2007, 26(5), 339-351.
[http://dx.doi.org/10.1089/dna.2006.0551] [PMID: 17504029]
[17]
Muthusamy, S.K.; Dalal, M.; Chinnusamy, V.; Bansal, K.C. Genome-wide identification and analysis of biotic and abiotic stress regulation of small heat shock protein (HSP20) family genes in bread wheat. J. Plant Physiol., 2017, 211, 100-113.
[http://dx.doi.org/10.1016/j.jplph.2017.01.004] [PMID: 28178571]
[18]
Nam, J. W.; Kim, J.; Kim, S.K.; Zhang, B.T. ProMiR II: A web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs. Nucleic Acids Res., 2006, 34((Web Server issue), W455-W458.
[http://dx.doi.org/10.1093/nar/gkl321]
[19]
Terai, G.; Komori, T.; Asai, K.; Kin, T. miRRim: A novel system to find conserved miRNAs with high sensitivity and specificity. RNA, 2007, 13(12), 2081-2090.
[http://dx.doi.org/10.1261/rna.655107] [PMID: 17959929]
[20]
Kadri, S.; Hinman, V.; Benos, P.V. HHMMiR: Efficient de novo prediction of microRNAs using hierarchical hidden Markov models. BMC Bioinformatics, 2009, 10(Suppl. 1), S35.
[http://dx.doi.org/10.1186/1471-2105-10-S1-S35] [PMID: 19208136]
[21]
Yousef, M.; Nebozhyn, M.; Shatkay, H.; Kanterakis, S.; Showe, L.C.; Showe, M.K. Combining multi-species genomic data for microRNA identification using a Naive Bayes classifier. Bioinformatics, 2006, 22(11), 1325-1334.
[http://dx.doi.org/10.1093/bioinformatics/btl094] [PMID: 16543277]
[22]
Chang, D.T.; Wang, C.C.; Chen, J.W. Using a kernel density estimation-based classifier to predict species-specific microRNA precursors. BMC Bioinformatics, 2008, 9(Suppl. 12), S2.
[http://dx.doi.org/10.1186/1471-2105-9-S12-S2] [PMID: 19091019]
[23]
Kleftogiannis, D.; Korfiati, A.; Theofilatos, K.; Likothanassis, S.; Tsakalidis, A.; Mavroudi, S. Where we stand, where we are moving: Surveying computational techniques for identifying miRNA genes and uncovering their regulatory role. J. Biomed. Inform., 2013, 46(3), 563-573.
[http://dx.doi.org/10.1016/j.jbi.2013.02.002] [PMID: 23501016]
[24]
Bandyopadhyay, S.; Ghosh, D.; Mitra, R.; Zhao, Z. MBSTAR: Multiple instance learning for predicting specific functional binding sites in microRNA targets. Sci. Rep., 2015, 5, 8004.
[http://dx.doi.org/10.1038/srep08004] [PMID: 25614300]
[25]
Ding, J.; Li, X.; Hu, H. TarPmiR: A new approach for microRNA target site prediction. Bioinformatics, 2016, 32(18), 2768-2775.
[http://dx.doi.org/10.1093/bioinformatics/btw318] [PMID: 27207945]
[26]
Yousef, M.; Jung, S.; Kossenkov, A.V.; Showe, L.C.; Showe, M.K. Naïve Bayes for microRNA target predictions-machine learning for microRNA targets. Bioinformatics, 2007, 23(22), 2987-2992.
[http://dx.doi.org/10.1093/bioinformatics/btm484] [PMID: 17925304]
[27]
Williams, A.M.; Liu, Y.; Regner, K.R.; Jotterand, F.; Liu, P.; Liang, M. Artificial intelligence, physiological genomics, and precision medicine. Physiol. Genomics, 2018, 50(4), 237-243.
[http://dx.doi.org/10.1152/physiolgenomics.00119.2017] [PMID: 29373082]
[28]
Orange, D.E.; Agius, P.; DiCarlo, E.F.; Robine, N.; Geiger, H.; Szymonifka, J.; McNamara, M.; Cummings, R.; Andersen, K.M.; Mirza, S.; Figgie, M.; Ivashkiv, L.; Pernis, A.B.; Jiang, C.; Frank, M.; Darnell, R.; Lingampali, N.; William, R.; Gravallese, E.; Bykerk, V.P.; Goodman, S.M.; Donlin, L.T. Identification of three rheumatoid arthritis disease subtypes by machine learning integration of synovial histologic features and RNA sequencing data. Arthritis Rheumatol., 2018, 70(5), 690-701.
[http://dx.doi.org/10.1002/art.40428]
[29]
Huang, W.; Sherman, B.T.; Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 2009, 4(1), 44-57.
[http://dx.doi.org/10.1038/nprot.2008.211] [PMID: 19131956]
[30]
Wong, N.; Wang, X. miRDB: An online resource for microRNA target prediction and functional annotations. Nucleic Acids Res., 2015, 43(Database issue), D146-D152.
[http://dx.doi.org/10.1093/nar/gku1104] [PMID: 25378301]
[31]
Sales, G.; Coppe, A.; Bisognin, A.; Biasiolo, M.; Bortoluzzi, S.; Romualdi, C. MAGIA, a web-based tool for miRNA and genes integrated analysis. Nucleic Acids Res, 2010, 38((Web Server issue), W352-W359.
[http://dx.doi.org/10.1093/nar/gkq423]
[32]
Ulitsky, I.; Laurent, L.C.; Shamir, R. Towards computational prediction of microRNA function and activity. Nucleic Acids Res., 2010, 38(15)e160
[http://dx.doi.org/10.1093/nar/gkq570] [PMID: 20576699]
[33]
Huang, J.C.; Morris, Q.D.; Frey, B.J. Bayesian inference of MicroRNA targets from sequence and expression data. J. Comput. Biol., 2007, 14(5), 550-563.
[http://dx.doi.org/10.1089/cmb.2007.R002] [PMID: 17683260]
[34]
Tang, J.; Liu, R.; Zhang, Y.L.; Liu, M.Z.; Hu, Y.F.; Shao, M.J.; Zhu, L.J.; Xin, H.W.; Feng, G.W.; Shang, W.J.; Meng, X.G.; Zhang, L.R.; Ming, Y.Z.; Zhang, W. Application of machine-learning models to predict tacrolimus stable dose in renal transplant recipients. Sci. Rep., 2017, 7, 42192.
[http://dx.doi.org/10.1038/srep42192] [PMID: 28176850]
[35]
Chen, W.H.; Hsieh, S.L.; Hsu, K.P.; Chen, H.P.; Su, X.Y.; Tseng, Y.J.; Chien, Y.H.; Hwu, W.L.; Lai, F. Web-based newborn screening system for metabolic diseases: machine learning versus clinicians. J. Med. Internet Res., 2013, 15(5) e98
[http://dx.doi.org/10.2196/jmir.2495] [PMID: 23702487]

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