Figure 1. Overall architecture of the SpliceEM framework (IMAGE)
Caption
Figure 1. Overall architecture of the SpliceEM framework. (A) Heterogeneous Graph Construction: Integrates multi-modal features (sequences, GO terms, and AS events) into a unified topological network; (B) HGT-based Message Passing: Captures cross-modal interactions via a Heterogeneous Graph Transformer to extract discriminative isoform embeddings; (C) EMA-MIL Joint Optimization: Employs a teacher-student framework with gene-masked ASL and hierarchical constraints to achieve robust isoform function prediction. GO: gene ontology; AS: alternative splicing; HGT: heterogeneous graph transformer; EMA: exponential moving average; MIL: multi-instance learning; ASL: asymmetric loss; BERT: bidirectional encoder representations from transformers; ESM: evolutionary scale modeling; BCE: binary cross-entropy.
Credit
© Gu T, Wang J, 2026. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Usage Restrictions
Credit must be given to the creator.
License
CC BY