Graph Neural Networks For Biological Knowledge Discovery
Résumé
Introduction:
Biological data is both abundant and heterogeneous. Consequently, it benefits from non-Euclidean structures like graphs for representation. This study examines how Graph Neural Networks (GNNs) perform in biological knowledge discovery compared to traditional geometric models.
Biological data is both abundant and heterogeneous. Consequently, it benefits from non-Euclidean structures like graphs for representation. This study examines how Graph Neural Networks (GNNs) perform in biological knowledge discovery compared to traditional geometric models.
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