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Items: 16

1.

Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures.

Fu X, Zhu W, Cai L, Liao B, Peng L, Chen Y, Yang J.

Front Genet. 2019 Feb 25;10:119. doi: 10.3389/fgene.2019.00119. eCollection 2019.

2.

miPIE: NGS-based Prediction of miRNA Using Integrated Evidence.

Peace RJ, Sheikh Hassani M, Green JR.

Sci Rep. 2019 Feb 7;9(1):1548. doi: 10.1038/s41598-018-38107-z.

3.

A comprehensive review of web-based resources of non-coding RNAs for plant science research.

Liao P, Li S, Cui X, Zheng Y.

Int J Biol Sci. 2018 May 22;14(8):819-832. doi: 10.7150/ijbs.24593. eCollection 2018. Review.

4.

Distinguishing mirtrons from canonical miRNAs with data exploration and machine learning methods.

Rorbach G, Unold O, Konopka BM.

Sci Rep. 2018 May 15;8(1):7560. doi: 10.1038/s41598-018-25578-3.

5.

MicroRNAs in Taenia solium Neurocysticercosis: Insights as Promising Agents in Host-Parasite Interaction and Their Potential as Biomarkers.

Gutierrez-Loli R, Orrego MA, Sevillano-Quispe OG, Herrera-Arrasco L, Guerra-Giraldez C.

Front Microbiol. 2017 Sep 29;8:1905. doi: 10.3389/fmicb.2017.01905. eCollection 2017. Review.

6.

On the performance of pre-microRNA detection algorithms.

Saçar Demirci MD, Baumbach J, Allmer J.

Nat Commun. 2017 Aug 24;8(1):330. doi: 10.1038/s41467-017-00403-z.

7.

An improved method for identification of small non-coding RNAs in bacteria using support vector machine.

Barman RK, Mukhopadhyay A, Das S.

Sci Rep. 2017 Apr 6;7:46070. doi: 10.1038/srep46070.

8.

Delineating the impact of machine learning elements in pre-microRNA detection.

Saçar Demirci MD, Allmer J.

PeerJ. 2017 Mar 29;5:e3131. doi: 10.7717/peerj.3131. eCollection 2017.

9.

Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction.

Marques YB, de Paiva Oliveira A, Ribeiro Vasconcelos AT, Cerqueira FR.

BMC Bioinformatics. 2016 Dec 15;17(Suppl 18):474. doi: 10.1186/s12859-016-1343-8. Erratum in: BMC Bioinformatics. 2017 Feb 17;18(1):113.

10.

Automatic learning of pre-miRNAs from different species.

O N Lopes Id, Schliep A, de L F de Carvalho AP.

BMC Bioinformatics. 2016 May 28;17(1):224. doi: 10.1186/s12859-016-1036-3.

11.

Improved Pre-miRNA Classification by Reducing the Effect of Class Imbalance.

Zhong Y, Xuan P, Han K, Zhang W, Li J.

Biomed Res Int. 2015;2015:960108. doi: 10.1155/2015/960108. Epub 2015 Nov 10.

12.

A framework for improving microRNA prediction in non-human genomes.

Peace RJ, Biggar KK, Storey KB, Green JR.

Nucleic Acids Res. 2015 Nov 16;43(20):e138. doi: 10.1093/nar/gkv698. Epub 2015 Jul 10.

13.

miRBoost: boosting support vector machines for microRNA precursor classification.

Tran Vdu T, Tempel S, Zerath B, Zehraoui F, Tahi F.

RNA. 2015 May;21(5):775-85. doi: 10.1261/rna.043612.113. Epub 2015 Mar 20.

14.

The discriminant power of RNA features for pre-miRNA recognition.

Lopes Ide O, Schliep A, de Carvalho AC.

BMC Bioinformatics. 2014 May 2;15:124. doi: 10.1186/1471-2105-15-124.

15.

High-throughput sequencing identification of novel and conserved miRNAs in the Brassica oleracea leaves.

Lukasik A, Pietrykowska H, Paczek L, Szweykowska-Kulinska Z, Zielenkiewicz P.

BMC Genomics. 2013 Nov 19;14:801. doi: 10.1186/1471-2164-14-801.

16.

miRNEST 2.0: a database of plant and animal microRNAs.

Szczesniak MW, Makalowska I.

Nucleic Acids Res. 2014 Jan;42(Database issue):D74-7. doi: 10.1093/nar/gkt1156. Epub 2013 Nov 15.

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