An automated framework for efficiently designing deep convolutional neural networks in genomics-Reference-Cited by-同舟云学术

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An automated framework for efficiently designing deep convolutional neural networks in genomics

Published:2021-03-15 Issue:5 Volume:3 Page:392-400
ISSN:2522-5839
Container-title:Nature Machine Intelligence
language:en
Short-container-title:Nat Mach Intell

Author:

Zhang Zijun^ORCID,Park Christopher Y.,Theesfeld Chandra L.,Troyanskaya Olga G.^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Link

http://www.nature.com/articles/s42256-021-00316-z.pdf

Reference47 articles.

1. Eraslan, G., Avsec, Ž., Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. https://doi.org/10.1038/s41576-019-0122-6 (2019).

2. Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15, 20170387 (2018).

3. LeCun, Y. & Bengio, Y. in The Handbook of Brain Theory and Neural Networks (ed. Arbib, M. A.) 3361(10) (MIT Press, 1995).

4. Zhou, J. & Troyanskaya, O. G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12, 931–934 (2015).

5. Kelley, D. R., Snoek, J. & Rinn, J. L. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res 26, 990–999 (2016).

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