Poster Open Access

Using attention-based neural networks to enable explainable drug sensitivity prediction on multimodal data

Manica Matteo; Oskooei Ali; Born Jannis; Subramanian Vigneshwari; Saez-Rodriguez Julio; Rodriguez Martinez Maria

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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3374375", 
  "title": "Using attention-based neural networks to enable explainable drug sensitivity prediction on multimodal data", 
  "issued": {
    "date-parts": [
      [
        2019, 
        8, 
        22
      ]
    ]
  }, 
  "abstract": "<p>PaccMann tackles the challenging problem of drug sensitivity prediction adopting a holistic approach.<br>\nThe model was trained on data from Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/)</p>", 
  "author": [
    {
      "family": "Manica Matteo"
    }, 
    {
      "family": "Oskooei Ali"
    }, 
    {
      "family": "Born Jannis"
    }, 
    {
      "family": "Subramanian Vigneshwari"
    }, 
    {
      "family": "Saez-Rodriguez Julio"
    }, 
    {
      "family": "Rodriguez Martinez Maria"
    }
  ], 
  "id": "3374375", 
  "event-place": "Basel, Switzerland", 
  "type": "graphic", 
  "event": "27th Conference on Intelligent Systems for Molecular Biology and the 18th European Conference on Computational Biology (ISMB/ECCB 2019)"
}
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Publication date:
August 22, 2019
DOI:
10.5281/zenodo.3374375

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Keyword(s):
drug sensitivity
Grants:
European Commission:
  • iPC - individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology (826121)
Meeting:
27th Conference on Intelligent Systems for Molecular Biology and the 18th European Conference on Computational Biology (ISMB/ECCB 2019), Basel, Switzerland, 21-25 July 2019
Communities:
License (for files):
Creative Commons Attribution 4.0 International

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