Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge
This provides a tool for biomedical researchers to incorporate prior knowledge into automated machine learning, making deep learning more accessible, though it is incremental as it adapts existing NAS methods to a specific domain.
The authors tackled the problem of building interpretable deep learning models for biomedical research by developing BioNAS, a neural architecture search framework that jointly optimizes predictive power and biological knowledge, demonstrating its ability to reveal novel knowledge in simulated and real functional genomics data.
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.