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Items: 1 to 20 of 113

1.

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.

2.

MaturePred: efficient identification of microRNAs within novel plant pre-miRNAs.

Xuan P, Guo M, Huang Y, Li W, Huang Y.

PLoS One. 2011;6(11):e27422. doi: 10.1371/journal.pone.0027422. Epub 2011 Nov 16.

3.

Prediction of plant pre-microRNAs and their microRNAs in genome-scale sequences using structure-sequence features and support vector machine.

Meng J, Liu D, Sun C, Luan Y.

BMC Bioinformatics. 2014 Dec 30;15:423. doi: 10.1186/s12859-014-0423-x.

4.

microPred: effective classification of pre-miRNAs for human miRNA gene prediction.

Batuwita R, Palade V.

Bioinformatics. 2009 Apr 15;25(8):989-95. doi: 10.1093/bioinformatics/btp107. Epub 2009 Feb 20.

PMID:
19233894
5.

miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences.

Cui H, Zhai J, Ma C.

PLoS One. 2015 Nov 11;10(11):e0142753. doi: 10.1371/journal.pone.0142753. eCollection 2015.

6.

PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs.

Xuan P, Guo M, Liu X, Huang Y, Li W, Huang Y.

Bioinformatics. 2011 May 15;27(10):1368-76. doi: 10.1093/bioinformatics/btr153. Epub 2011 Mar 26.

PMID:
21441575
7.

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.

8.
9.

Improving classification of mature microRNA by solving class imbalance problem.

Wang Y, Li X, Tao B.

Sci Rep. 2016 May 16;6:25941. doi: 10.1038/srep25941.

10.

MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features.

Ding J, Zhou S, Guan J.

BMC Bioinformatics. 2010 Dec 14;11 Suppl 11:S11. doi: 10.1186/1471-2105-11-S11-S11.

11.

High Class-Imbalance in pre-miRNA Prediction: A Novel Approach Based on deepSOM.

Stegmayer G, Yones C, Kamenetzky L, Milone DH.

IEEE/ACM Trans Comput Biol Bioinform. 2017 Nov-Dec;14(6):1316-1326. doi: 10.1109/TCBB.2016.2576459. Epub 2016 Jun 7.

PMID:
27295687
12.

Ab initio identification of human microRNAs based on structure motifs.

Brameier M, Wiuf C.

BMC Bioinformatics. 2007 Dec 18;8:478.

13.

MiRANN: a reliable approach for improved classification of precursor microRNA using Artificial Neural Network model.

Rahman ME, Islam R, Islam S, Mondal SI, Amin MR.

Genomics. 2012 Apr;99(4):189-94. doi: 10.1016/j.ygeno.2012.02.001. Epub 2012 Feb 10.

14.

Ensemble-based classification approach for micro-RNA mining applied on diverse metagenomic sequences.

ElGokhy SM, ElHefnawi M, Shoukry A.

BMC Res Notes. 2014 May 6;7:286. doi: 10.1186/1756-0500-7-286.

15.

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.

16.

plantMirP: an efficient computational program for the prediction of plant pre-miRNA by incorporating knowledge-based energy features.

Yao Y, Ma C, Deng H, Liu Q, Zhang J, Yi M.

Mol Biosyst. 2016 Oct 20;12(10):3124-31. doi: 10.1039/c6mb00295a. Epub 2016 Jul 29.

PMID:
27472470
17.

Effective classification of microRNA precursors using feature mining and AdaBoost algorithms.

Zhong L, Wang JT, Wen D, Aris V, Soteropoulos P, Shapiro BA.

OMICS. 2013 Sep;17(9):486-93. doi: 10.1089/omi.2013.0011. Epub 2013 Jun 29.

18.

Effective sample selection for classification of pre-miRNAs.

Han K.

Genet Mol Res. 2011 Mar 29;10(1):506-18. doi: 10.4238/vol10-1gmr1054.

19.

Computational prediction of the localization of microRNAs within their pre-miRNA.

Leclercq M, Diallo AB, Blanchette M.

Nucleic Acids Res. 2013 Aug;41(15):7200-11. doi: 10.1093/nar/gkt466. Epub 2013 Jun 8.

20.

HuntMi: an efficient and taxon-specific approach in pre-miRNA identification.

Gudyś A, Szcześniak MW, Sikora M, Makałowska I.

BMC Bioinformatics. 2013 Mar 5;14:83. doi: 10.1186/1471-2105-14-83.

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