Abstract
MicroRNAs (miRNAs) are non-coding RNAs of ~21–23 bases 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. This chapter introduces a new technique to model microRNA-target binding using Recurrent Neural Networks (RNN) over a miRNA-target duplex sequence representation.
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References
Bartel, D. (2009). MicroRNAs: Target Recognition and Regulatory Functions. Cell. 136(2), PP.215-233.
Bartel, D. (2004). MicroRNAs: Genomics, Biogenesis, Mechanism and Function. Cell. 116, PP.281-297
Xu, B., Hsu, P., Karayiorgou, M. and Gogos, J. (2012). MicroRNA dysregulation in neuropsychiatric disorders and cognitive dysfunction. Neurobiology of Disease, 46(2), pp.291- 301.
Oğul, H., Umu, S., Tuncel, Y. and Akkaya, M. (2011). A probabilistic approach to microRNA-target binding. Biochemical and Biophysical Research Communications, 413(1), pp.111-115.
Wen, M., Cong, P., Zhang, Z., Lu, H. and Li, T. (2018). DeepMirTar: a deep-learning approach for predicting human miRNA targets. Bioinformatics, 34(22), pp.3781-3787.
Needleman, S.B. and Wunsch, C.D. (1970). A general method applicable to the search for similarities in the amino acid sequences of two proteins. J. Mol. Biol., 48, 443–453.
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, (2015).
D. Ron, Y.Singer, N. Tishby, The power of amnesia: learning probabilistic automata with variable memory length, Mach. Learn 25 (1996) 117-149
Menor, M., et al. (2014) mirMark: a site-level and UTR-level classifier for miRNA target prediction. Genome biology,15,500
Helwak, A., et al. (2013) Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell, 153, 654-665
D Dede, H Oğul, TriClust: A Tool for Cross-Species Analysis of Gene Regulation, Molecular Informatics 33 (5), 382-387
H.Oğul, M.S.Akkaya, (2011), Data integration in functional analysis of microRNAs, Current Bioinformatics 6, 462-472.
M. Rehmsmeier, P. Steffen, M. Hochmann, et al., Fast and effective prediction of microRNA/target duplexes, RNA 10 (2004) 1507–1517.
M. Hammell, Computational methods to identify miRNA targets, Semin. Cell Dev. Biol. 21 (2010) 738–744.
Y. Yang, Y.P. Wang, K.B. Li, miRTif: a support vector machine-based microRNA target interaction filter, BMC Bioinf. 9 (2008) S4.
Kingma, D.P., & Ba, J. (2014). Adam: A Method for Stochastic Optimization. CoRR, abs/1412.6980.
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735– 80
Hinton, G.E.; Salakhutdinov, R.R. Reducing the dimensionality of data with neural network. Science 2006, 7, 504–507.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning representations by backpropagating errors. Nature, 323, 533–536.
M. D. Zeiler, Adadelta: an adaptive learning rate method. arXiv preprint (2012). arXiv:1212.5701
Paker, Ahmet & Ogul, Hasan (2019). mirLSTM: A Deep Sequential Approach to MicroRNA Target Binding Site Prediction. Communications in Computer and Information Science 1062, s 38- 44
C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 2O:l-25, 1995.
Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. Belmont, California: Wadsworth (1984)
Devroye, L. & Wagner, T.J. (1982) "Nearest neighbor methods in discrimination, In Classification, Pattern Recognition and Reduction of Dimensionality", Handbook of Statistics, 2: 193–197. North-Holland, Amsterdam
Breiman, L. Random forests. Machine Learning, 45(1):5–32, 2001.
Douglas Montgomery, Peck, E., & Vinning, G. (2012). Introduction to Linear Regression Analysis (5th ed.). Wiley.
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Paker, A., Oğul, H. (2021). A Deep Learning Model for MicroRNA-Target Binding. In: Elloumi, M. (eds) Deep Learning for Biomedical Data Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-71676-9_3
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DOI: https://doi.org/10.1007/978-3-030-71676-9_3
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