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Deep learning for regulatory genomics

Nature Biotechnology volume 33, pages 825–826 (2015)Cite this article

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Computational modeling of DNA and RNA targets of regulatory proteins is improved by a deep-learning approach.

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Figure 1: Illustration of the deep convolutional neural network designed by Alipanahi et al.1.

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Authors and Affiliations

  1. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Yongjin Park & Manolis Kellis

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  1. Yongjin Park
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  2. Manolis Kellis
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Correspondence to Manolis Kellis.

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The authors declare no competing financial interests.

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Park, Y., Kellis, M. Deep learning for regulatory genomics. Nat Biotechnol 33, 825–826 (2015). https://doi.org/10.1038/nbt.3313

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