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Integrative approaches to improve the informativeness of deep learning models for human complex diseases

View ORCID ProfileKushal K. Dey, Samuel S. Kim, Steven Gazal, Joseph Nasser, Jesse M. Engreitz, Alkes L. Price
doi: https://doi.org/10.1101/2020.09.08.288563
Kushal K. Dey
1Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
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  • ORCID record for Kushal K. Dey
  • For correspondence: kshldey@gmail.com
Samuel S. Kim
1Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
2Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
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Steven Gazal
1Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
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Joseph Nasser
3Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Jesse M. Engreitz
3Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Alkes L. Price
1Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
3Broad Institute of MIT and Harvard, Cambridge, MA, USA
4Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
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  • https://github.com/kkdey/Imperio

  • https://alkesgroup.broadinstitute.org/LDSCORE/DeepLearning/Dey_DeepBoost_Imperio/

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Posted August 13, 2021.
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Integrative approaches to improve the informativeness of deep learning models for human complex diseases
Kushal K. Dey, Samuel S. Kim, Steven Gazal, Joseph Nasser, Jesse M. Engreitz, Alkes L. Price
bioRxiv 2020.09.08.288563; doi: https://doi.org/10.1101/2020.09.08.288563
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Integrative approaches to improve the informativeness of deep learning models for human complex diseases
Kushal K. Dey, Samuel S. Kim, Steven Gazal, Joseph Nasser, Jesse M. Engreitz, Alkes L. Price
bioRxiv 2020.09.08.288563; doi: https://doi.org/10.1101/2020.09.08.288563

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