• English
  • Deutsch
  • Log In
    or
  • Research Outputs
  • Projects
  • Researchers
  • Institutes
  • Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Artikel
  4. GuiltyTargets: Prioritization of Novel Therapeutic Targets with Deep Network Representation Learning
 
  • Details
  • Full
Options
2020
Journal Article
Titel

GuiltyTargets: Prioritization of Novel Therapeutic Targets with Deep Network Representation Learning

Abstract
Objective: The majority of clinical trials fail due to low efficacy of investigated drugs, often resulting from a poor choice of target protein. Existing computational approaches aim to support target selection either via genetic evidence or by putting potential targets into the context of disease specific network reconstructions.
Author(s)
Muslu, Özlem
Hoyt, Charles Tapley
Lacerda, Mauricio Pio de
Hofmann-Apitius, Martin
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Fröhlich, Holger
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Zeitschrift
IEEE ACM transactions on computational biology and bioinformatics
Funder
Fraunhofer-Gesellschaft FhG
Thumbnail Image
DOI
10.1109/TCBB.2020.3003830
Language
English
google-scholar
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Tags
  • artificial intelligen...

  • neural networks

  • bioinformatic

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Send Feedback
© 2022