  We report a neural architecture search framework, BioNAS, that is tailored
for biomedical researchers to easily build, evaluate, and uncover novel
knowledge from interpretable deep learning models. The introduction of
knowledge dissimilarity functions in BioNAS enables the joint optimization of
predictive power and biological knowledge through searching architectures in a
model space. By optimizing the consistency with existing knowledge, we
demonstrate that BioNAS optimal models reveal novel knowledge in both simulated
data and in real data of functional genomics. BioNAS provides a useful tool for
domain experts to inject their prior belief into automated machine learning and
therefore making deep learning easily accessible to practitioners. BioNAS is
available at https://github.com/zj-zhang/BioNAS-pub.
