Nature Communications (Sep 2020)

Evaluating the informativeness of deep learning annotations for human complex diseases

  • Kushal K. Dey,
  • Bryce van de Geijn,
  • Samuel Sungil Kim,
  • Farhad Hormozdiari,
  • David R. Kelley,
  • Alkes L. Price

DOI
https://doi.org/10.1038/s41467-020-18515-4
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 9

Abstract

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Deep learning models have shown great promise in predicting regulatory effects from DNA sequence. Here the authors evaluate sequence-based epigenomic deep learning models and conclude that these models are not yet ready to inform our knowledge of human disease.