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How Severe is Your COVID-19? Predicting SARS-CoV-2 Infection with Graph Attention Capsule Networks
2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2022 ; : 751-754, 2022.
Article in English | Scopus | ID: covidwho-2327440
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 a 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. Significantly 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 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. © 2022 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study Language: English Journal: WIC Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study Language: English Journal: WIC Year: 2022 Document Type: Article