Poster Open Access

Interpretability for computational biology

Nguyen An-phi; Rodriguez-Martinez


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{
  "description": "<p>Why do we need interpretability to unveil the decision process ofa machine learning model?<br>\nTrust - for high-risk scenarios, e.g. healthcare, the user needs to trust the decision taken.<br>\nDebugging -&nbsp;the model may be badly trained or there might be an unfair bias in either the dataset or the model itself.<br>\nHypothesis generation - surprising results might be consequences of new mechanisms or patterns unknown even to field experts.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "@type": "Person", 
      "name": "Nguyen An-phi"
    }, 
    {
      "@type": "Person", 
      "name": "Rodriguez-Martinez"
    }
  ], 
  "url": "https://zenodo.org/record/3374361", 
  "datePublished": "2019-08-22", 
  "@type": "CreativeWork", 
  "keywords": [
    "computational biology"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.3374361", 
  "@id": "https://doi.org/10.5281/zenodo.3374361", 
  "workFeatured": {
    "url": "https://www.iscb.org/ismbeccb2019-program/tutorials", 
    "alternateName": "ISMB/ECCB 2019", 
    "location": "Basel, Switzerland", 
    "@type": "Event", 
    "name": "27th Conference on Intelligent Systems for Molecular Biology and the 18th European Conference on Computational Biology"
  }, 
  "name": "Interpretability for computational biology"
}
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