Tabular biomedical data poses challenges in machine learning because it is
often high-dimensional and typically low-sample-size. Previous research has
attempted to address these challenges via feature selection approaches, which
can lead to unstable performance on real-world data. This suggests that current
methods lack appropriate inductive biases that capture patterns common to
different samples. In this paper, we propose ProtoGate, a prototype-based
neural model that introduces an inductive bias by attending to both homogeneity
and heterogeneity across samples. ProtoGate selects features in a
global-to-local manner and leverages them to produce explainable predictions
via an interpretable prototype-based model. We conduct comprehensive
experiments to evaluate the performance of ProtoGate on synthetic and
real-world datasets. Our results show that exploiting the homogeneous and
heterogeneous patterns in the data can improve prediction accuracy while
prototypes imbue interpretability.

本研究提出了 ProtoGate，一种基于原型的神经网络模型，通过全局到局部选择特征并利用这些特征生成可解释的预测，识别数据中的同质性和异质性模式来改善预测精度。对合成和真实世界数据集进行了综合实验，结果表明，利用数据中的同质性和异质性模式可以提高预测精度，而原型增加了可解释性。