Applications of Machine Learning in MiRNA Discovery and Target Prediction

Overview

Journal Curr Genomics

Publisher Bentham Science Publishers

Date 2020 Jun 26

PMID 32581642

Citations 15

Authors

Alisha Parveen

Syed H Mustafa

Pankaj Yadav

Abhishek Kumar

Affiliations

Soon will be listed here.

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.

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References

Nam J, Kim J, Kim S, Zhang B . ProMiR II: a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs. Nucleic Acids Res. 2006; 34(Web Server issue):W455-8. PMC: 1538778. DOI: 10.1093/nar/gkl321. View

Orange D, Agius P, DiCarlo E, Robine N, Geiger H, Szymonifka J . 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. PMC: 6336443. DOI: 10.1002/art.40428. View

Allmer J, Yousef M . Computational miRNomics. J Integr Bioinform. 2017; 13(5):1-2. DOI: 10.1515/jib-2016-302. View

Lindow M, Gorodkin J . Principles and limitations of computational microRNA gene and target finding. DNA Cell Biol. 2007; 26(5):339-51. DOI: 10.1089/dna.2006.0551. View

Tang J, Liu R, Zhang Y, Liu M, Hu Y, Shao M . Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients. Sci Rep. 2017; 7:42192. PMC: 5296901. DOI: 10.1038/srep42192. View

Lee R, Feinbaum R, Ambros V . The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993; 75(5):843-54. DOI: 10.1016/0092-8674(93)90529-y. View

Kadri S, Hinman V, Benos P . HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models. BMC Bioinformatics. 2009; 10 Suppl 1:S35. PMC: 2648761. DOI: 10.1186/1471-2105-10-S1-S35. View

Williams A, Liu Y, Regner K, Jotterand F, Liu P, Liang M . Artificial intelligence, physiological genomics, and precision medicine. Physiol Genomics. 2018; 50(4):237-243. PMC: 5966805. DOI: 10.1152/physiolgenomics.00119.2017. View

Parveen A, Gretz N, Dweep H . Obtaining miRNA-Target Interaction Information from miRWalk2.0. Curr Protoc Bioinformatics. 2016; 55:12.15.1-12.15.27. DOI: 10.1002/cpbi.14. View

10.

Ulitsky I, Laurent L, Shamir R . Towards computational prediction of microRNA function and activity. Nucleic Acids Res. 2010; 38(15):e160. PMC: 2926627. DOI: 10.1093/nar/gkq570. View

11.

Huang J, Morris Q, Frey B . Bayesian inference of MicroRNA targets from sequence and expression data. J Comput Biol. 2007; 14(5):550-63. DOI: 10.1089/cmb.2007.R002. View

12.

Tien-Hao Chang D, Wang C, Chen J . Using a kernel density estimation based classifier to predict species-specific microRNA precursors. BMC Bioinformatics. 2008; 9 Suppl 12:S2. PMC: 2638167. DOI: 10.1186/1471-2105-9-S12-S2. View

13.

Ha M, Kim V . Regulation of microRNA biogenesis. Nat Rev Mol Cell Biol. 2014; 15(8):509-24. DOI: 10.1038/nrm3838. View

14.

Ding J, Li X, Hu H . TarPmiR: a new approach for microRNA target site prediction. Bioinformatics. 2016; 32(18):2768-75. PMC: 5018371. DOI: 10.1093/bioinformatics/btw318. View

15.

Chen Q, Cao F . Distributed support vector machine in master-slave mode. Neural Netw. 2018; 101:94-100. DOI: 10.1016/j.neunet.2018.02.006. View

16.

Sebastian B, Aggrey S . Specificity and sensitivity of PROMIR, ERPIN and MIR-ABELA in predicting pre-microRNAs in the chicken genome. In Silico Biol. 2009; 8(5-6):377-81. View

17.

Lorenz R, Bernhart S, Honer Zu Siederdissen C, Tafer H, Flamm C, Stadler P . ViennaRNA Package 2.0. Algorithms Mol Biol. 2011; 6:26. PMC: 3319429. DOI: 10.1186/1748-7188-6-26. View

18.

Kozomara A, Griffiths-Jones S . miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2013; 42(Database issue):D68-73. PMC: 3965103. DOI: 10.1093/nar/gkt1181. View

19.

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):W352-9. PMC: 2896126. DOI: 10.1093/nar/gkq423. View

20.

Wong N, Wang X . miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res. 2014; 43(Database issue):D146-52. PMC: 4383922. DOI: 10.1093/nar/gku1104. View