Presentation Open Access

How in silico methods and data reuse can help reduce the number of animal experiments

Maddalena Fratelli


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  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>Presentation at the conference&nbsp;&nbsp;&quot;Towards replacement of animals for scientific purposes&quot; ,&nbsp; 2-3 february 2021.</p>\n\n<p>Talking about machine learning to develop models of drug sensitivity prediction, (Q)SAR models of toxicity prediction and the role of data re-use in enhancing external validity of the studies.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Istituto di Ricerche Farmacologiche Mario Negri IRCCS", 
      "@id": "https://orcid.org/0000-0002-1769-3427", 
      "@type": "Person", 
      "name": "Maddalena Fratelli"
    }
  ], 
  "url": "https://zenodo.org/record/4524007", 
  "datePublished": "2021-02-09", 
  "keywords": [
    "machine learning, in silico methods, data re.use, European Open Science Cloud, external validity"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.4524007", 
  "@id": "https://doi.org/10.5281/zenodo.4524007", 
  "@type": "PresentationDigitalDocument", 
  "name": "How in silico methods and data reuse can help reduce the number of animal experiments"
}
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