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Tabular biomedical data poses challenges in machine learning because it isoften high-dimensional and typically low-sample-size. Previous research hasattempted to address these challenges via feature selection approaches, whichcan lead to unstable performance on real-world data. This suggests that currentmethods lack appropriate inductive biases that capture patterns common todifferent samples. In this paper, we propose ProtoGate, a prototype-basedneural model that introduces an inductive bias by attending to both homogeneityand heterogeneity across samples. ProtoGate selects features in aglobal-to-local manner and leverages them to produce explainable predictionsvia an interpretable prototype-based model. We conduct comprehensiveexperiments to evaluate the performance of ProtoGate on synthetic andreal-world datasets. Our results show that exploiting the homogeneous andheterogeneous patterns in the data can improve prediction accuracy whileprototypes imbue interpretability.

ProtoGate: Prototype-based Neural Networks with Local Feature Selection for Tabular Biomedical Data