Poster Open Access
Interpretability for computational biology
Nguyen An-phi; Rodriguez-Martinez
Why do we need interpretability to unveil the decision process ofa machine learning model?
Trust - for high-risk scenarios, e.g. healthcare, the user needs to trust the decision taken.
Debugging - the model may be badly trained or there might be an unfair bias in either the dataset or the model itself.
Hypothesis generation - surprising results might be consequences of new mechanisms or patterns unknown even to field experts.
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interpretability.pdf
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Indexed in
- Publication date:
- August 22, 2019
- DOI:
-
- Keyword(s):
- computational biology
- Grants:
-
European Commission:
- iPC - individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology (826121)
- Meeting:
- 27th Conference on Intelligent Systems for Molecular Biology and the 18th European Conference on Computational Biology (ISMB/ECCB 2019), Basel, Switzerland, 21-25 July 2019
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International