research-article

deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks

Authors:

Seunghyun Park,

Sungroh YoonAuthors Info & Claims

BCB '16: Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Pages 434 - 442

https://doi.org/10.1145/2975167.2975212

Published: 02 October 2016 Publication History

Abstract

MicroRNAs (miRNAs) are short sequences of ribonucleic acids that control the expression of target messenger RNAs (mRNAs) by binding them. Robust prediction of miRNA-mRNA pairs is of utmost importance in deciphering gene regulation but has been challenging because of high false positive rates, despite a deluge of computational tools that normally require laborious manual feature extraction. This paper presents an end-to-end machine learning framework for miRNA target prediction. Leveraged by deep recurrent neural networks-based auto-encoding and sequence-sequence interaction learning, our approach not only delivers an unprecedented level of accuracy but also eliminates the need for manual feature extraction. The performance gap between the proposed method and existing alternatives is substantial (over 25% increase in F-measure), and deepTarget delivers a quantum leap in the longstanding challenge of robust miRNA target prediction. [availability: http://data.snu.ac.kr/pub/deepTarget]

References

[1]

D. Bahdanau, K. Cho, and Y. Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014.

[2]

P. Baldi and S. Brunak. Chapter 6. neural networks: applications. In Bioinformatics: The Machine Learning Approach. MIT press, 2001.

Digital Library

[3]

S. Bandyopadhyay, D. Ghosh, R. Mitra, and Z. Zhao. MB-STAR: multiple instance learning for predicting specific functional binding sites in microRNA targets. Scientific reports, 5, 2015.

[4]

D. P. Bartel. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 116(2):281--297, 2004.

[5]

S. Cheng, M. Guo, C. Wang, X. Liu, Y. Liu, and X. Wu. MiRTDL: a deep learning approach for miRNA target prediction.

[6]

K. Cho, B. van Merriënboer, D. Bahdanau, and Y. Bengio. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259, 2014.

[7]

K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014.

[8]

F. Chollet. Keras: Deep Learning library for Theano and TensorFlow. https://github.com/fchollet/keras, 2015.

[9]

A. J. Enright, B. John, U. Gaul, T. Tuschl, C. Sander, D. S. Marks, et al. MicroRNA targets in Drosophila. Genome Biology, 5(1):R1--R1, 2004.

[10]

R. A. Fisher, F. Yates, et al. Statistical tables for biological, agricultural and medical research. Statistical tables for biological, agricultural and medical research., (Ed. 3.), 1949.

[11]

R. C. Friedman, K. K.-H. Farh, C. B. Burge, and D. P. Bartel. Most mammalian mRNAs are conserved targets of microRNAs. Genome Research, 19(1):92--105, 2009.

[12]

X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. In International conference on artificial intelligence and statistics, pages 249--256, 2010.

[13]

I. Goodfellow, Y. Bengio, and A. Courville. Deep learning. Book in preparation for MIT Press, 2016.

Digital Library

[14]

K. Gregor, I. Danihelka, A. Graves, and D. Wierstra. DRAW: A recurrent neural network for image generation. arXiv preprint arXiv:1502.04623, 2015.

[15]

S. Griffiths-Jones, H. K. Saini, S. van Dongen, and A. J. Enright. miRBase: tools for microRNA genomics. Nucleic Acids Research, 36(suppl 1):D154--D158, 2008.

[16]

S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Computation, 9(8):1735--1780, 1997.

Digital Library

[17]

S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015.

Digital Library

[18]

B. John, A. J. Enright, A. Aravin, T. Tuschl, C. Sander, D. S. Marks, et al. Human microRNA targets. PLoS Biol, 2(11):e363, 2004.

[19]

R. Jozefowicz, W. Zaremba, and I. Sutskever. An empirical exploration of recurrent network architectures. In Proceedings of the 32nd International Conference on Machine Learning (ICML-15), pages 2342--2350, 2015.

Digital Library

[20]

M. Kertesz, N. Iovino, U. Unnerstall, U. Gaul, and E. Segal. The role of site accessibility in microRNA target recognition. Nature Genetics, 39(10):1278--1284, 2007.

[21]

D. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.

[22]

D. P. Kingma and M. Welling. Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114, 2013.

[23]

Q. V. Le, N. Jaitly, and G. E. Hinton. A simple way to initialize recurrent networks of rectified linear units. arXiv preprint arXiv:1504.00941, 2015.

[24]

B. Lee, T. Lee, B. Na, and S. Yoon. DNA-level splice junction prediction using deep recurrent neural networks. arXiv preprint arXiv:1512.05135, 2015.

[25]

T. Lee and S. Yoon. Boosted categorical restricted boltzmann machine for computational prediction of splice junctions. In ICML, pages 2483--2492, 2015.

[26]

B. P. Lewis, I.-h. Shih, M. W. Jones-Rhoades, D. P. Bartel, and C. B. Burge. Prediction of mammalian microRNA targets. Cell, 115(7):787--798, 2003.

[27]

J. Lu, G. Getz, E. A. Miska, E. Alvarez-Saavedra, J. Lamb, D. Peck, A. Sweet-Cordero, B. L. Ebert, R. H. Mak, A. A. Ferrando, et al. MicroRNA expression profiles classify human cancers. nature, 435(7043):834--838, 2005.

[28]

T. M Witkos, E. Koscianska, and W. J Krzyzosiak. Practical aspects of microRNA target prediction. Current molecular medicine, 11(2):93--109, 2011.

[29]

M. Maragkakis, P. Alexiou, G. L. Papadopoulos, M. Reczko, T. Dalamagas, G. Giannopoulos, G. Goumas, E. Koukis, K. Kourtis, V. A. Simossis, et al. Accurate microRNA target prediction correlates with protein repression levels. BMC Bioinformatics, 10(1):295, 2009.

[30]

P. Maziere and A. J. Enright. Prediction of microRNA targets. Drug discovery today, 12(11):452--458, 2007.

[31]

M. Menor, T. Ching, X. Zhu, D. Garmire, and L. X. Garmire. mirMark: a site-level and UTR-level classifier for miRNA target prediction. Genome biology, 15(10):500, 2014.

[32]

H. Min and S. Yoon. Got target?: Computational methods for microRNA target prediction and their extension. Experimental & Molecular Medicine, 42(4):233--244, 2010.

[33]

S. Min, B. Lee, and S. Yoon. Deep learning in bioinformatics. Briefings in Bioinformatics, in press, 2016.

[34]

K. C. Miranda, T. Huynh, Y. Tay, Y.-S. Ang, W.-L. Tam, A. M. Thomson, B. Lim, and I. Rigoutsos. A pattern-based method for the identification of microRNA binding sites and their corresponding heteroduplexes. Cell, 126(6):1203--1217, 2006.

[35]

J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng. Multimodal deep learning. In Proceedings of the 28th international conference on machine learning (ICML-11), pages 689--696, 2011.

Digital Library

[36]

R. Pascanu, C. Gulcehre, K. Cho, and Y. Bengio. How to construct deep recurrent neural networks. arXiv preprint arXiv:1312.6026, 2013.

[37]

S. M. Peterson, J. A. Thompson, M. L. Ufkin, P. Sathya-narayana, L. Liaw, and C. B. Congdon. Common features of microRNA target prediction tools. Front Genet, 5:23, 2014.

[38]

T. Saito and M. Rehmsmeier. The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PloS One, 10(3):e0118432, 2015.

[39]

H. Sak, A. Senior, and F. Beaufays. Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In Proceedings of the Annual Conference of International Speech Communication Association (INTERSPEECH), 2014.

[40]

M. Schuster and K. K. Paliwal. Bidirectional recurrent neural networks. Signal Processing, IEEE Transactions on, 45(11):2673--2681, 1997.

Digital Library

[41]

A. Shrikumar, P. Greenside, A. Shcherbina, and A. Kundaje. Not just a black box: Learning important features through propagating activation differences. arXiv preprint arXiv:1605.01713, 2016.

[42]

K. Sohn, W. Shang, and H. Lee. Improved multimodal deep learning with variation of information. In Advances in Neural Information Processing Systems, pages 2141--2149, 2014.

Digital Library

[43]

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929--1958, 2014.

Digital Library

[44]

N. Srivastava, E. Mansimov, and R. Salakhutdinov. Unsupervised learning of video representations using LSTMs. arXiv preprint arXiv:1502.04681, 2015.

[45]

P. K. Srivastava, T. R. Moturu, P. Pandey, I. T. Baldwin, and S. P. Pandey. A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction. BMC Genomics, 15(1):1, 2014.

[46]

M. Sturm, M. Hackenberg, D. Langenberger, and D. Frishman. TargetSpy: a supervised machine learning approach for microRNA target prediction. BMC Bioinformatics, 11(1):292, 2010.

[47]

P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, pages 1096--1103. ACM, 2008.

Digital Library

[48]

O. Vinyals, A. Toshev, S. Bengio, and D. Erhan. Show and tell: A neural image caption generator. arXiv preprint arXiv:1411.4555, 2014.

[49]

F. Xiao, Z. Zuo, G. Cai, S. Kang, X. Gao, and T. Li. miRecords: an integrated resource for microRNA--target interactions. Nucleic Acids Research, 37(suppl 1):D105--D110, 2009.

[50]

S. Yoon and G. De Micheli. Computational identification of microRNAs and their targets. Birth Defects Research Part C: Embryo Today: Reviews, 78(2):118--128, 2006.

Cited By

Yoon SYoon HCho JLee K(2024)AEmiGAP: AutoEncoder-Based miRNA–Gene Association Prediction Using Deep Learning MethodInternational Journal of Molecular Sciences10.3390/ijms25231307525:23(13075)Online publication date: 5-Dec-2024
https://doi.org/10.3390/ijms252313075
Chakraborty CBhattacharya MRanjan Sharma A(2024)miRNA, siRNA, and lncRNA: Recent Development of Bioinformatics Tools and Databases in Support of Combating Different DiseasesCurrent Bioinformatics10.2174/157489361866623041110494519:1(39-60)Online publication date: Feb-2024
https://doi.org/10.2174/1574893618666230411104945
Yang TWang YHe Y(2024)TEC-miTarget: enhancing microRNA target prediction based on deep learning of ribonucleic acid sequencesBMC Bioinformatics10.1186/s12859-024-05780-z25:1Online publication date: 20-Apr-2024
https://doi.org/10.1186/s12859-024-05780-z
Show More Cited By

Index Terms

deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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cover image ACM Conferences

BCB '16: Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

October 2016

675 pages

ISBN:9781450342254

DOI:10.1145/2975167

Copyright © 2016 ACM.

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Published: 02 October 2016

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Cited By

Yoon SYoon HCho JLee K(2024)AEmiGAP: AutoEncoder-Based miRNA–Gene Association Prediction Using Deep Learning MethodInternational Journal of Molecular Sciences10.3390/ijms25231307525:23(13075)Online publication date: 5-Dec-2024
https://doi.org/10.3390/ijms252313075
Chakraborty CBhattacharya MRanjan Sharma A(2024)miRNA, siRNA, and lncRNA: Recent Development of Bioinformatics Tools and Databases in Support of Combating Different DiseasesCurrent Bioinformatics10.2174/157489361866623041110494519:1(39-60)Online publication date: Feb-2024
https://doi.org/10.2174/1574893618666230411104945
Yang TWang YHe Y(2024)TEC-miTarget: enhancing microRNA target prediction based on deep learning of ribonucleic acid sequencesBMC Bioinformatics10.1186/s12859-024-05780-z25:1Online publication date: 20-Apr-2024
https://doi.org/10.1186/s12859-024-05780-z
Liu PLiu YLuo JLi Y(2024)MiRGraph: A hybrid deep learning approach to identify microRNA-target interactions by integrating heterogeneous regulatory network and genomic sequences2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10822436(1028-1035)Online publication date: 3-Dec-2024
https://doi.org/10.1109/BIBM62325.2024.10822436
Bi YLi FWang CPan TDavidovich CWebb GSong J(2024)Advancing microRNA target site prediction with transformer and base-pairing patternsNucleic Acids Research10.1093/nar/gkae78252:19(11455-11465)Online publication date: 13-Sep-2024
https://doi.org/10.1093/nar/gkae782
Mathuria AMehak Mani I(2024)Role of Bioinformatics in Non-coding RNA AnalysisAdvances in Bioinformatics10.1007/978-981-99-8401-5_5(113-136)Online publication date: 6-Feb-2024
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Orhan MDemirci YSaçar Demirci M(2024)Bioinformatics Tools to Study the Role of miRNAsmiRNAs, Human Health and Diseases10.1007/978-3-031-64788-8_3(41-60)Online publication date: 29-Sep-2024
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Chakraborty SRay Dutta JGanesan RMinary P(2024)The Evolution of Nucleic Acid–Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA DesignRNA Design10.1007/978-1-0716-4079-1_17(241-300)Online publication date: 24-Sep-2024
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