These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

160 related items for PubMed ID: 27472470

  • 1. 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. PubMed ID: 27472470
    [Abstract] [Full Text] [Related]

  • 2. PlantMirP-Rice: An Efficient Program for Rice Pre-miRNA Prediction.
    Zhang H, Wang H, Yao Y, Yi M.
    Genes (Basel); 2020 Jun 18; 11(6):. PubMed ID: 32570706
    [Abstract] [Full Text] [Related]

  • 3. 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. PubMed ID: 21441575
    [Abstract] [Full Text] [Related]

  • 4. MaturePred: efficient identification of microRNAs within novel plant pre-miRNAs.
    Xuan P, Guo M, Huang Y, Li W, Huang Y.
    PLoS One; 2011 May 15; 6(11):e27422. PubMed ID: 22110646
    [Abstract] [Full Text] [Related]

  • 5. 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(1):423. PubMed ID: 25547126
    [Abstract] [Full Text] [Related]

  • 6. microPred: effective classification of pre-miRNAs for human miRNA gene prediction.
    Batuwita R, Palade V.
    Bioinformatics; 2009 Apr 15; 25(8):989-95. PubMed ID: 19233894
    [Abstract] [Full Text] [Related]

  • 7. PlantMirP2: An Accurate, Fast and Easy-To-Use Program for Plant Pre-miRNA and miRNA Prediction.
    Fan D, Yao Y, Yi M.
    Genes (Basel); 2021 Aug 21; 12(8):. PubMed ID: 34440454
    [Abstract] [Full Text] [Related]

  • 8. miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences.
    Cui H, Zhai J, Ma C.
    PLoS One; 2015 Aug 21; 10(11):e0142753. PubMed ID: 26558614
    [Abstract] [Full Text] [Related]

  • 9. Predicting human microRNA precursors based on an optimized feature subset generated by GA-SVM.
    Wang Y, Chen X, Jiang W, Li L, Li W, Yang L, Liao M, Lian B, Lv Y, Wang S, Wang S, Li X.
    Genomics; 2011 Aug 21; 98(2):73-8. PubMed ID: 21586321
    [Abstract] [Full Text] [Related]

  • 10. Genetic algorithm-based efficient feature selection for classification of pre-miRNAs.
    Xuan P, Guo MZ, Wang J, Wang CY, Liu XY, Liu Y.
    Genet Mol Res; 2011 Apr 12; 10(2):588-603. PubMed ID: 21491369
    [Abstract] [Full Text] [Related]

  • 11. Identification of microRNA precursors with support vector machine and string kernel.
    Xu JH, Li F, Sun QF.
    Genomics Proteomics Bioinformatics; 2008 Jun 12; 6(2):121-8. PubMed ID: 18973868
    [Abstract] [Full Text] [Related]

  • 12. miRNAFinder: A comprehensive web resource for plant Pre-microRNA classification.
    Lokuge S, Jayasundara S, Ihalagedara P, Kahanda I, Herath D.
    Biosystems; 2022 Jun 12; 215-216():104662. PubMed ID: 35306049
    [Abstract] [Full Text] [Related]

  • 13. 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. PubMed ID: 27233515
    [Abstract] [Full Text] [Related]

  • 14. ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features.
    Meher PK, Begam S, Sahu TK, Gupta A, Kumar A, Kumar U, Rao AR, Singh KP, Dhankher OP.
    Int J Mol Sci; 2022 Jan 30; 23(3):. PubMed ID: 35163534
    [Abstract] [Full Text] [Related]

  • 15. Bioinformatics Study of Structural Patterns in Plant MicroRNA Precursors.
    Miskiewicz J, Tomczyk K, Mickiewicz A, Sarzynska J, Szachniuk M.
    Biomed Res Int; 2017 Jan 30; 2017():6783010. PubMed ID: 28280737
    [Abstract] [Full Text] [Related]

  • 16. 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 29; 6():310. PubMed ID: 16381612
    [Abstract] [Full Text] [Related]

  • 17. New syntax to describe local continuous structure-sequence information for recognizing new pre-miRNAs.
    Wang M, Song X, Han P, Li W, Jiang B.
    J Theor Biol; 2010 May 21; 264(2):578-84. PubMed ID: 20202471
    [Abstract] [Full Text] [Related]

  • 18. Computational prediction and experimental verification of miRNAs in Panicum miliaceum L.
    Wu Y, Du J, Wang X, Fang X, Shan W, Liang Z.
    Sci China Life Sci; 2012 Sep 21; 55(9):807-17. PubMed ID: 23015130
    [Abstract] [Full Text] [Related]

  • 19. PMirP: a pre-microRNA prediction method based on structure-sequence hybrid features.
    Zhao D, Wang Y, Luo D, Shi X, Wang L, Xu D, Yu J, Liang Y.
    Artif Intell Med; 2010 Jun 21; 49(2):127-32. PubMed ID: 20399081
    [Abstract] [Full Text] [Related]

  • 20. 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(Suppl 11):S11. PubMed ID: 21172046
    [Abstract] [Full Text] [Related]

    Page: [Next] [New Search]
    of 8.