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Article citations


Ng, K.L.S. and Mishra, S.K. (2007) De Novo SVM Classification of Precursor microRNAs from Genomic Pseudo Hairpins Using Global and Intrinsic Folding Measures. Bioinformatics, 23, 1321-1330.

has been cited by the following article:

  • TITLE: Accurate Plant MicroRNA Prediction Can Be Achieved Using Sequence Motif Features

    AUTHORS: Malik Yousef, Jens Allmer, Waleed Khalifa

    KEYWORDS: MicroRNA Prediction, Plant, Bioinformatics, Machine Learning, Sequence Motifs

    JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.8 No.1, December 28, 2015

    ABSTRACT: MicroRNAs (miRNAs) are short (~21 nt) nucleotide sequences that are either co-transcribed during the production of mRNA or are organized in intergenic regions transcribed by RNA polymerase II. In animals, Drosha, and in plants DCL1 recognize pre-miRNAs which set themselves apart by their characteristic stem loop (hairpin) structure. This structure appears important for their recognition during the process of maturation leading to functioning mature miRNAs. A large body of research is available for computational pre-miRNA detection in animals, but less within the plant kingdom. For the prediction of pre-miRNAs, usually machine learning approaches are employed. Therefore, it is necessary to convert the pre-miRNAs into a set of features that can be calculated and many such features have been described. We here select a subset of the previously described features and add sequence motifs as new features. The resulting model which we called MotifmiRNAPred was tested on known pre-miRNAs listed in miRBase and its accuracy was compared to existing approaches in the field. With an accuracy of 99.95% for the generalized plant model, it distinguishes itself from previously published results which reach an average accuracy between 74% and 98%. We believe that our approach is useful for prediction of pre-miRNAs in plants without per species adjustment.