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Journal Abstract Search

81 related items for PubMed ID: 21476196

  • 1. Effective sample selection for classification of pre-miRNAs.
    Han K.
    Genet Mol Res; 2011 Mar 29; 10(1):506-18. PubMed ID: 21476196
    [Abstract] [Full Text] [Related]

  • 2. 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]

  • 3. 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]

  • 4. 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]

  • 5. 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 15; 98(2):73-8. PubMed ID: 21586321
    [Abstract] [Full Text] [Related]

  • 6. De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures.
    Ng KL, Mishra SK.
    Bioinformatics; 2007 Jun 01; 23(11):1321-30. PubMed ID: 17267435
    [Abstract] [Full Text] [Related]

  • 7. 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]

  • 8. 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 15; 49(2):127-32. PubMed ID: 20399081
    [Abstract] [Full Text] [Related]

  • 9. 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]

  • 10. Prediction of pre-miRNA with multiple stem-loops using pruning algorithm.
    Song X, Wang M, Chen YP, Wang H, Han P, Sun H.
    Comput Biol Med; 2013 Jun 21; 43(5):409-16. PubMed ID: 23566387
    [Abstract] [Full Text] [Related]

  • 11. MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data.
    Mathelier A, Carbone A.
    Bioinformatics; 2010 Sep 15; 26(18):2226-34. PubMed ID: 20591903
    [Abstract] [Full Text] [Related]

  • 12. 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 15; 35(Web Server issue):W339-44. PubMed ID: 17553836
    [Abstract] [Full Text] [Related]

  • 13. Exploring cross-species-related miRNAs based on sequence and secondary structure.
    Chen F, Chen YP.
    IEEE Trans Biomed Eng; 2010 Jul 15; 57(7):1547-53. PubMed ID: 20199930
    [Abstract] [Full Text] [Related]

  • 14. Improving classification of mature microRNA by solving class imbalance problem.
    Wang Y, Li X, Tao B.
    Sci Rep; 2016 May 16; 6():25941. PubMed ID: 27181057
    [Abstract] [Full Text] [Related]

  • 15. OP-Triplet-ELM: Identification of real and pseudo microRNA precursors using extreme learning machine with optimal features.
    Pian C, Zhang J, Chen YY, Chen Z, Li Q, Li Q, Zhang LY.
    J Bioinform Comput Biol; 2016 Feb 16; 14(1):1650006. PubMed ID: 26707924
    [Abstract] [Full Text] [Related]

  • 16. Variable predictive model based classification algorithm for effective separation of protein structural classes.
    Raghuraj R, Lakshminarayanan S.
    Comput Biol Chem; 2008 Aug 16; 32(4):302-6. PubMed ID: 18462997
    [Abstract] [Full Text] [Related]

  • 17. MaturePred: efficient identification of microRNAs within novel plant pre-miRNAs.
    Xuan P, Guo M, Huang Y, Li W, Huang Y.
    PLoS One; 2011 Aug 16; 6(11):e27422. PubMed ID: 22110646
    [Abstract] [Full Text] [Related]

  • 18. 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]

  • 19. Improving gene expression cancer molecular pattern discovery using nonnegative principal component analysis.
    Han X.
    Genome Inform; 2008 Oct 20; 21():200-11. PubMed ID: 19425159
    [Abstract] [Full Text] [Related]

  • 20. Computational identification of novel microRNAs and targets in Brassica napus.
    Xie FL, Huang SQ, Guo K, Xiang AL, Zhu YY, Nie L, Yang ZM.
    FEBS Lett; 2007 Apr 03; 581(7):1464-74. PubMed ID: 17367786
    [Abstract] [Full Text] [Related]

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