AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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S2DA-GO alleviates feature interference and improves prediction for sparsely annotated GO terms, providing a promising framework for large-scale annotation of uncharacterized proteins.
DHST is proposed, a deep hybrid structure–topology framework that integrates sequence semantics from a pretrained protein language model with local structural information learned by a residual graph convolutional network and introduces site-specific persistent homology to encode multi-scale topological invariants and a topology-guided residue-wise gated fusion module to modulate structure–semantics representations using local topological embeddings.
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