预测能力和生物知识联合优化的神经结构搜索
Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge
摘要：
我们报告了一个神经架构搜索框架BioNAS，它是为生物医学研究人员量身定制的，可以轻松地从可解释的深度学习模型中构建、评估和发现新知识。在BioNAS中引入知识相异性函数，通过在模型空间中搜索架构，实现了预测能力和生物知识的联合优化。通过优化与现有知识的一致性，我们证明了BioNAS优化模型揭示了功能基因组学模拟数据和真实数据中的新知识。BioNAS为领域专家提供了一个有用的工具，可以将他们先前的信念注入自动化机器学习，从而使深度学习更容易为从业者所用。BioNAS可在https://github.com/zj-zhang/BioNAS-pub上获得。
英文摘要：
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.
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