from-cells-to-drug-responses-machine-learning-in-cancer-research-julian-de-ruiter-nanne-aben.json

{
  "copyright_text": null,
  "description": "Machine learning plays an important role in cancer research. In this talk, we\u2019ll tackle the challenge of predicting which patients are likely to respond to given anti-cancer treatments. In doing so, we\u2019ll show how tools such as Snakemake/Bioconda can be used to create reproducible workflows and illustrate the challenges of interpreting predictive models in large, highly-correlated feature spaces.",
  "duration": 2077,
  "language": "eng",
  "recorded": "2018-05-26",
  "related_urls": [
    {
      "label": "Conference schedule",
      "url": "https://pydata.org/amsterdam2018/schedule/"
    },
    {
      "label": "Talk slides",
      "url": "https://www.slideshare.net/PyData/from-cells-to-drug-responses-machine-learning-in-cancer-research-julian-de-ruiter-nanne-aben"
    }
  ],
  "speakers": [
    "Julian de Ruiter",
    "Nanne Aben"
  ],
  "tags": [],
  "thumbnail_url": "https://i.ytimg.com/vi/oedAGH_LE4U/maxresdefault.jpg",
  "title": "From cells to drug responses - machine learning in cancer research",
  "videos": [
    {
      "type": "youtube",
      "url": "https://www.youtube.com/watch?v=oedAGH_LE4U"
    }
  ]
}