ABSTRACT Recent studies in machine learning have demonstrated the effectiveness of applying graph neural networks (GNNs) to single‐cell RNA sequencing (scRNA‐seq) data to predict COVID‐19 disease states. In this study, we propose an explainable graph attention capsule network (GACapNet), which extracts and fuses Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2) transcriptomic patterns to improve node classification performance on cells and genes. Different from the existing GNN approaches, we innovatively incorporate a capsule layer with dynamic routing into our model architecture to combine and fuse gene features effectively and to allow those more prominent gene features to be present in the output. We evaluate our GACapNet model on two scRNA‐seq datasets, and the experimental results show that our GACapNet model significantly outperforms state‐of‐the‐art baseline models. Therefore, our study demonstrates the capability of advanced machine learning models to generate predictive features and evolutionary patterns of the SARS‐CoV‐2 pathogen, and the applicability of closing knowledge gaps in the pathogenesis and recovery of COVID‐19. The source code is available on GitHub at https://github.com/mdamranhossenbhuiyan/GACapNet .
Zhu et al. (Tue,) studied this question.