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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Autores principales:	Kushal K. Dey, Bryce van de Geijn, Samuel Sungil Kim, Farhad Hormozdiari, David R. Kelley, Alkes L. Price
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/e966c41c45b24c86a35cad7f34f42b80
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spelling	oai:doaj.org-article:e966c41c45b24c86a35cad7f34f42b802021-12-02T17:24:13ZEvaluating the informativeness of deep learning annotations for human complex diseases10.1038/s41467-020-18515-42041-1723https://doaj.org/article/e966c41c45b24c86a35cad7f34f42b802020-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-18515-4https://doaj.org/toc/2041-1723Deep 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.Kushal K. DeyBryce van de GeijnSamuel Sungil KimFarhad HormozdiariDavid R. KelleyAlkes L. PriceNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Kushal K. Dey Bryce van de Geijn Samuel Sungil Kim Farhad Hormozdiari David R. Kelley Alkes L. Price Evaluating the informativeness of deep learning annotations for human complex diseases
description	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.
format	article
author	Kushal K. Dey Bryce van de Geijn Samuel Sungil Kim Farhad Hormozdiari David R. Kelley Alkes L. Price
author_facet	Kushal K. Dey Bryce van de Geijn Samuel Sungil Kim Farhad Hormozdiari David R. Kelley Alkes L. Price
author_sort	Kushal K. Dey
title	Evaluating the informativeness of deep learning annotations for human complex diseases
title_short	Evaluating the informativeness of deep learning annotations for human complex diseases
title_full	Evaluating the informativeness of deep learning annotations for human complex diseases
title_fullStr	Evaluating the informativeness of deep learning annotations for human complex diseases
title_full_unstemmed	Evaluating the informativeness of deep learning annotations for human complex diseases
title_sort	evaluating the informativeness of deep learning annotations for human complex diseases
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/e966c41c45b24c86a35cad7f34f42b80
work_keys_str_mv	AT kushalkdey evaluatingtheinformativenessofdeeplearningannotationsforhumancomplexdiseases AT brycevandegeijn evaluatingtheinformativenessofdeeplearningannotationsforhumancomplexdiseases AT samuelsungilkim evaluatingtheinformativenessofdeeplearningannotationsforhumancomplexdiseases AT farhadhormozdiari evaluatingtheinformativenessofdeeplearningannotationsforhumancomplexdiseases AT davidrkelley evaluatingtheinformativenessofdeeplearningannotationsforhumancomplexdiseases AT alkeslprice evaluatingtheinformativenessofdeeplearningannotationsforhumancomplexdiseases
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Evaluating the informativeness of deep learning annotations for human complex diseases

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