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>", 
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    "keywords": [
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    "publication_date": "2019-08-22", 
    "creators": [
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      "dates": "21-25 July 2019", 
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