  Tabular biomedical data poses challenges in machine learning because it is
often high-dimensional and typically low-sample-size (HDLSS). Previous research
has attempted to address these challenges via local feature selection, but
existing approaches often fail to achieve optimal performance due to their
limitation in identifying globally important features and their susceptibility
to the co-adaptation problem. In this paper, we propose ProtoGate, a
prototype-based neural model for feature selection on HDLSS data. ProtoGate
first selects instance-wise features via adaptively balancing global and local
feature selection. Furthermore, ProtoGate employs a non-parametric
prototype-based prediction mechanism to tackle the co-adaptation problem,
ensuring the feature selection results and predictions are consistent with
underlying data clusters. We conduct comprehensive experiments to evaluate the
performance and interpretability of ProtoGate on synthetic and real-world
datasets. The results show that ProtoGate generally outperforms
state-of-the-art methods in prediction accuracy by a clear margin while
providing high-fidelity feature selection and explainable predictions. Code is
available at https://github.com/SilenceX12138/ProtoGate.
