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We report a neural architecture search framework, BioNAS, that is tailoredfor biomedical researchers to easily build, evaluate, and uncover novelknowledge from interpretable deep learning models. The introduction ofknowledge dissimilarity functions in BioNAS enables the joint optimization ofpredictive power and biological knowledge through searching architectures in amodel space. By optimizing the consistency with existing knowledge, wedemonstrate that BioNAS optimal models reveal novel knowledge in both simulateddata and in real data of functional genomics. BioNAS provides a useful tool fordomain experts to inject their prior belief into automated machine learning andtherefore making deep learning easily accessible to practitioners. BioNAS isavailable at https://github.com/zj-zhang/BioNAS-pub.

Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge