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DOUBLER: Unified Representation Learning of Biological Entities and Documents for Predicting Protein–Disease Relationships

View ORCID ProfileTimo Sztyler, View ORCID ProfileBrandon Malone
doi: https://doi.org/10.1101/2020.10.27.357202
Timo Sztyler
1NEC Laboratories Europe, Heidelberg, 69115, Germany
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  • For correspondence: timo.sztyler@neclab.eu
Brandon Malone
1NEC Laboratories Europe, Heidelberg, 69115, Germany
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Article Information

doi 
https://doi.org/10.1101/2020.10.27.357202
History 
  • October 27, 2020.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Author Information

  1. Timo Sztyler1,* and
  2. Brandon Malone1
  1. 1NEC Laboratories Europe, Heidelberg, 69115, Germany
  1. ↵*To whom correspondence should be addressed. email: timo.sztyler{at}neclab.eu
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DOUBLER: Unified Representation Learning of Biological Entities and Documents for Predicting Protein–Disease Relationships
Timo Sztyler, Brandon Malone
bioRxiv 2020.10.27.357202; doi: https://doi.org/10.1101/2020.10.27.357202
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DOUBLER: Unified Representation Learning of Biological Entities and Documents for Predicting Protein–Disease Relationships
Timo Sztyler, Brandon Malone
bioRxiv 2020.10.27.357202; doi: https://doi.org/10.1101/2020.10.27.357202

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