Evaluating the informativeness of deep learning annotations for human complex diseases
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.
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Auteurs principaux: | Kushal K. Dey, Bryce van de Geijn, Samuel Sungil Kim, Farhad Hormozdiari, David R. Kelley, Alkes L. Price |
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Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2020
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Sujets: | |
Accès en ligne: | https://doaj.org/article/e966c41c45b24c86a35cad7f34f42b80 |
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