ricardo-corral-corral-constructing-protein-structural-features-for-machine-learning.json

{
  "description": "Proteins are the most abundant macromolecules on cells. They perform a\nwide range of biological activities due to its adopted three dimensional\nstructures. First requirement to make use of Machine Learning\ntechnologies on this context, is to construct an informative set of\nfeatures for representing protein structures. We make use of the Residue\nCluster Class System, a labeled Sperner Family arising from atomic\npositions, giving a total set of 26 features. Practical applications are\npresented for various classical computational biology tasks. Entire code\nbase is implemented on Python as an API and ready to use final user\nprograms.\n",
  "duration": 2291,
  "language": "eng",
  "recorded": "2015-06-21",
  "related_urls": [
    {
      "label": "slides",
      "url": "http://162.243.152.57:7000/PyDataLDN2015f.pdf"
    },
    {
      "label": "tutorial material",
      "url": "https://github.com/RicardoCorralC/rccPyDataLondon2015"
    }
  ],
  "speakers": [
    "Ricardo Corral Corral"
  ],
  "summary": "An introduction to combinatorial construction of features for protein\nstructures and some practical applications and state of the art\nresults on task like structural and functional classification, decoy\nidentification, and fast finding of neighboring structures.\n\nSlides available here:http://162.243.152.57:7000/PyDataLDN2015f.pdf\n\nCode and quick tutorial: https://github.com/RicardoCorralC/rccPyDataLondon2015",
  "thumbnail_url": "https://i.ytimg.com/vi/QtcDY5Ts2uI/hqdefault.jpg",
  "title": "Constructing protein structural features for Machine Learning.",
  "videos": [
    {
      "type": "youtube",
      "url": "https://www.youtube.com/watch?v=QtcDY5Ts2uI"
    }
  ]
}