Poster Open Access

Interpretability for computational biology

Nguyen An-phi; Rodriguez-Martinez

JSON-LD ( Export

  "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": "", 
  "creator": [
      "@type": "Person", 
      "name": "Nguyen An-phi"
      "@type": "Person", 
      "name": "Rodriguez-Martinez"
  "url": "", 
  "datePublished": "2019-08-22", 
  "@type": "CreativeWork", 
  "keywords": [
    "computational biology"
  "@context": "", 
  "identifier": "", 
  "@id": "", 
  "workFeatured": {
    "url": "", 
    "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"
All versions This version
Views 176176
Downloads 187187
Data volume 49.4 MB49.4 MB
Unique views 163163
Unique downloads 181181


Cite as