Contributions
i) a novel large graph model that enhances peripheral GO term prediction by leveraging both explicit and implicit information, ii) a fusion strategy that improves overall GO annotation performance under imbalanced data conditions, and iii) interpretable representations that provide insights into the peripheral distribution problem in protein gene ontology annotation.
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