Abstract
MicroRNAs (miRNAs) are short (~22 nucleotides), endogenously-initiated non-coding RNAs that control gene expression post transcriptionally, either by the degradation of target miRNAs or by the inhibition of protein translation. The prediction of miRNA genes is a challenging problem towards the understanding of post transcriptional gene regulation. The present paper focuses on developing a computational method for the identification of miRNA precursors.
We propose a machine learning algorithm based on Random Forests (RF) for miRNA prediction. The prediction algorithm relies on a set of features; compiled from known features as well as others introduced for the first time; that results in a performance that is better than most well known miRNA classifiers. The method achieves 91.3% accuracy, 86% f-measure, 97.2% specificity, 93.4% precision and 79.6% sensitivity, when tested on real data. Our method succeeds in getting better results than MiPred (the best currently known RF algorithm in literature), Triplet-SVM and Virgo and EumiR.
The obtained results indicate that Random Forests is a better alternative to Support Vector Machines (SVM) for miRNA prediction, especially from the point of view of accuracy and f-measure metrics.
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ElGokhy, S.M., Shibuya, T., Shoukry, A. (2014). Improving miRNA Classification Using an Exhaustive Set of Features. In: Saez-Rodriguez, J., Rocha, M., Fdez-Riverola, F., De Paz Santana, J. (eds) 8th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014). Advances in Intelligent Systems and Computing, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-319-07581-5_4
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DOI: https://doi.org/10.1007/978-3-319-07581-5_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07580-8
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