363 related articles for article (PubMed ID: 28105918)
1. 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; 17(Suppl 18):474. PubMed ID: 28105918
[TBL] [Abstract][Full Text] [Related]
2. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine.
Xue C; Li F; He T; Liu GP; Li Y; Zhang X
BMC Bioinformatics; 2005 Dec; 6():310. PubMed ID: 16381612
[TBL] [Abstract][Full Text] [Related]
3. Erratum to: 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; 2017 Feb; 18(1):113. PubMed ID: 28212605
[No Abstract] [Full Text] [Related]
4. Ab initio identification of human microRNAs based on structure motifs.
Brameier M; Wiuf C
BMC Bioinformatics; 2007 Dec; 8():478. PubMed ID: 18088431
[TBL] [Abstract][Full Text] [Related]
5. Predicting novel microRNA: a comprehensive comparison of machine learning approaches.
Stegmayer G; Di Persia LE; Rubiolo M; Gerard M; Pividori M; Yones C; Bugnon LA; Rodriguez T; Raad J; Milone DH
Brief Bioinform; 2019 Sep; 20(5):1607-1620. PubMed ID: 29800232
[TBL] [Abstract][Full Text] [Related]
6. miRLocator: A Python Implementation and Web Server for Predicting miRNAs from Pre-miRNA Sequences.
Zhang T; Ju L; Zhai J; Song Y; Song J; Ma C
Methods Mol Biol; 2019; 1932():89-97. PubMed ID: 30701493
[TBL] [Abstract][Full Text] [Related]
7. 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. PubMed ID: 22110646
[TBL] [Abstract][Full Text] [Related]
8. Identification of clustered microRNAs using an ab initio prediction method.
Sewer A; Paul N; Landgraf P; Aravin A; Pfeffer S; Brownstein MJ; Tuschl T; van Nimwegen E; Zavolan M
BMC Bioinformatics; 2005 Nov; 6():267. PubMed ID: 16274478
[TBL] [Abstract][Full Text] [Related]
9. Genome-wide pre-miRNA discovery from few labeled examples.
Yones C; Stegmayer G; Milone DH
Bioinformatics; 2018 Feb; 34(4):541-549. PubMed ID: 29028911
[TBL] [Abstract][Full Text] [Related]
10. Ab initio human miRNA and pre-miRNA prediction.
Titov II; Vorozheykin PS
J Bioinform Comput Biol; 2013 Dec; 11(6):1343009. PubMed ID: 24372038
[TBL] [Abstract][Full Text] [Related]
11. A fast ab-initio method for predicting miRNA precursors in genomes.
Tempel S; Tahi F
Nucleic Acids Res; 2012 Jun; 40(11):e80. PubMed ID: 22362754
[TBL] [Abstract][Full Text] [Related]
12. Computational Detection of Pre-microRNAs.
Saçar Demirci MD
Methods Mol Biol; 2022; 2257():167-174. PubMed ID: 34432278
[TBL] [Abstract][Full Text] [Related]
13. miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences.
Cui H; Zhai J; Ma C
PLoS One; 2015; 10(11):e0142753. PubMed ID: 26558614
[TBL] [Abstract][Full Text] [Related]
14. 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; 15(1):423. PubMed ID: 25547126
[TBL] [Abstract][Full Text] [Related]
15. 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; 27(10):1368-76. PubMed ID: 21441575
[TBL] [Abstract][Full Text] [Related]
16. Benchmark comparison of ab initio microRNA identification methods and software.
Hu LL; Huang Y; Wang QC; Zou Q; Jiang Y
Genet Mol Res; 2012 Dec; 11(4):4525-38. PubMed ID: 23096922
[TBL] [Abstract][Full Text] [Related]
17. ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo
Meher PK; Begam S; Sahu TK; Gupta A; Kumar A; Kumar U; Rao AR; Singh KP; Dhankher OP
Int J Mol Sci; 2022 Jan; 23(3):. PubMed ID: 35163534
[TBL] [Abstract][Full Text] [Related]
18. MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features.
Jiang P; Wu H; Wang W; Ma W; Sun X; Lu Z
Nucleic Acids Res; 2007 Jul; 35(Web Server issue):W339-44. PubMed ID: 17553836
[TBL] [Abstract][Full Text] [Related]
19. microPred: effective classification of pre-miRNAs for human miRNA gene prediction.
Batuwita R; Palade V
Bioinformatics; 2009 Apr; 25(8):989-95. PubMed ID: 19233894
[TBL] [Abstract][Full Text] [Related]
20. Machine Learning Techniques in Exploring MicroRNA Gene Discovery, Targets, and Functions.
Singh S; Benton RG; Singh A; Singh A
Methods Mol Biol; 2017; 1617():211-224. PubMed ID: 28540688
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]