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Abstract Many real-world problems are naturally modeled as heterogeneous graphs, where nodes and edges represent multiple types of entities and relations. Existing learning models for heterogeneous graph representation usually depend on the computation of specific, user-defined heterogeneous paths, or on the applicatio...
By combining conditional pre-trained language model fine-tuning with dynamic structural evaluation, the framework advances beyond static sequence-based predictions to address the structural and functional dimensions of enzyme behavior, key aspects for both biomedical and industrial applications.
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