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


JSON Export

{
  "files": [
    {
      "links": {
        "self": "https://zenodo.org/api/files/a2fe5f39-f1f9-4ad8-b930-d7777c299a70/paccmann.pdf"
      }, 
      "checksum": "md5:f47f130a3add5a09031d60545f3e3c30", 
      "bucket": "a2fe5f39-f1f9-4ad8-b930-d7777c299a70", 
      "key": "paccmann.pdf", 
      "type": "pdf", 
      "size": 5068142
    }
  ], 
  "owners": [
    65392
  ], 
  "doi": "10.5281/zenodo.3374375", 
  "stats": {
    "version_unique_downloads": 79.0, 
    "unique_views": 104.0, 
    "views": 117.0, 
    "version_views": 117.0, 
    "unique_downloads": 79.0, 
    "version_unique_views": 104.0, 
    "volume": 405451360.0, 
    "version_downloads": 80.0, 
    "downloads": 80.0, 
    "version_volume": 405451360.0
  }, 
  "links": {
    "doi": "https://doi.org/10.5281/zenodo.3374375", 
    "conceptdoi": "https://doi.org/10.5281/zenodo.3374374", 
    "bucket": "https://zenodo.org/api/files/a2fe5f39-f1f9-4ad8-b930-d7777c299a70", 
    "conceptbadge": "https://zenodo.org/badge/doi/10.5281/zenodo.3374374.svg", 
    "html": "https://zenodo.org/record/3374375", 
    "latest_html": "https://zenodo.org/record/3374375", 
    "badge": "https://zenodo.org/badge/doi/10.5281/zenodo.3374375.svg", 
    "latest": "https://zenodo.org/api/records/3374375"
  }, 
  "conceptdoi": "10.5281/zenodo.3374374", 
  "created": "2019-08-22T11:57:31.850878+00:00", 
  "updated": "2020-01-20T17:08:25.081005+00:00", 
  "conceptrecid": "3374374", 
  "revision": 4, 
  "id": 3374375, 
  "metadata": {
    "access_right_category": "success", 
    "doi": "10.5281/zenodo.3374375", 
    "description": "<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>", 
    "license": {
      "id": "CC-BY-4.0"
    }, 
    "title": "Using attention-based neural networks to enable explainable drug sensitivity prediction on multimodal data", 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "3374374"
          }, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "3374375"
          }
        }
      ]
    }, 
    "communities": [
      {
        "id": "ipc"
      }
    ], 
    "grants": [
      {
        "code": "826121", 
        "links": {
          "self": "https://zenodo.org/api/grants/10.13039/501100000780::826121"
        }, 
        "title": "individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology", 
        "acronym": "iPC", 
        "program": "H2020", 
        "funder": {
          "doi": "10.13039/501100000780", 
          "acronyms": [], 
          "name": "European Commission", 
          "links": {
            "self": "https://zenodo.org/api/funders/10.13039/501100000780"
          }
        }
      }
    ], 
    "keywords": [
      "drug sensitivity"
    ], 
    "publication_date": "2019-08-22", 
    "creators": [
      {
        "name": "Manica Matteo"
      }, 
      {
        "name": "Oskooei Ali"
      }, 
      {
        "name": "Born Jannis"
      }, 
      {
        "name": "Subramanian Vigneshwari"
      }, 
      {
        "name": "Saez-Rodriguez Julio"
      }, 
      {
        "name": "Rodriguez Martinez Maria"
      }
    ], 
    "meeting": {
      "acronym": "ISMB/ECCB 2019", 
      "url": "https://www.iscb.org/ismbeccb2019-program/tutorials", 
      "dates": "21-25 July 2019", 
      "place": "Basel, Switzerland", 
      "title": "27th Conference on Intelligent Systems for Molecular Biology and the 18th European Conference on Computational Biology"
    }, 
    "access_right": "open", 
    "resource_type": {
      "type": "poster", 
      "title": "Poster"
    }, 
    "related_identifiers": [
      {
        "scheme": "doi", 
        "identifier": "10.5281/zenodo.3374374", 
        "relation": "isVersionOf"
      }
    ]
  }
}
117
80
views
downloads
All versions This version
Views 117117
Downloads 8080
Data volume 405.5 MB405.5 MB
Unique views 104104
Unique downloads 7979

Share

Cite as