Abstract
MicroRNAs (miRNAs) are small and non-coding RNAs of ~21–23 base length, which play critical role in gene expression. They bind the target mRNAs in the post-transcriptional level and cause translational inhibition or mRNA cleavage. Quick and effective detection of the binding sites of miRNAs is a major problem in bioinformatics. In this study, a deep learning approach based on Long Short Term Memory (LSTM) is developed with the help of an existing duplex sequence model. Compared with four conventional machine learning methods, the proposed LSTM model performs better in terms of the accuracy (ACC), sensitivity, specificity, AUC (Area under the curve) and F1 score. A web-tool is also developed to identify and display the microRNA target sites effectively and quickly.
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Bartel, D.: MicroRNAs: target recognition and regulatory functions. Cell 136(2), 215–233 (2009)
Bartel, D.: MicroRNAs: genomics, biogenesis, mechanism and function. Cell 116, 281–297 (2004)
Xu, B., Hsu, P., Karayiorgou, M., Gogos, J.: MicroRNA dysregulation in neuropsychiatric disorders and cognitive dysfunction. Neurobiol. Dis. 46(2), 291–301 (2012)
Oğul, H., Umu, S., Tuncel, Y., Akkaya, M.: A probabilistic approach to microRNA-target binding. Biochem. Biophys. Res. Commun. 413(1), 111–115 (2011)
Wen, M., Cong, P., Zhang, Z., Lu, H., Li, T.: DeepMirTar: a deep-learning approach for predicting human miRNA targets. Bioinformatics 34(22), 3781–3787 (2018)
Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequences of two proteins. J. Mol. Biol. 48, 443–453 (1970)
Ding, J., Li, X., Hu, H.: TarPmiR: a new approach for microRNA target site prediction. Bioinformatics 32, 2768–2775 (2016)
Agarwal, V., Bell, G., Nam, J., Bartel, D.: Predicting effective microRNA target sites in mammalian mRNAs. eLife 4, e05005 (2015)
Ron, D., Singer, Y., Tishby, N.: The power of amnesia: learning probabilistic automata with variable memory length. Mach. Learn. 25, 117–149 (1996)
Menor, M., et al.: mirMark: a site-level and UTR-level classifier for miRNA target prediction. Genome Biol. 15, 500 (2014)
Helwak, A., et al.: Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153, 654–665 (2013)
Dede, D., Oğul, H.: TriClust: a tool for cross-species analysis of gene regulation. Mol. Inf. 33(5), 382–387
Oğul, H., Akkaya, M.S.: Data integration in functional analysis of microRNAs. Curr. Bioinf. 6, 462–472 (2011)
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Paker, A., Oğul, H. (2019). mirLSTM: A Deep Sequential Approach to MicroRNA Target Binding Site Prediction. In: Anderst-Kotsis, G., et al. Database and Expert Systems Applications. DEXA 2019. Communications in Computer and Information Science, vol 1062. Springer, Cham. https://doi.org/10.1007/978-3-030-27684-3_6
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DOI: https://doi.org/10.1007/978-3-030-27684-3_6
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