S2 Table - CASynergy: A causal attention model for interpretable prediction of cancer drug synergy

<p>lists the hyperparameters optimized for the CASynergy model, including learning rates (10 ⁻ ² to 10 ⁻ ⁵), batch sizes (16–156), cross-attention layers (1–3), and global pooling methods (Max/Average). The final configuration was empirically determined through systematic validation.</p>...

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Bibliographic Details
Main Author:	Haitao Li (305080) (author)
Other Authors:	Long Zheng (694761) (author), Lei Li (29537) (author), Yiwei Chen (383344) (author), Junjie Li (12724) (author), Chunhou Zheng (15936521) (author), Yansen Su (459998) (author)
Published:	2025
Subjects:	Biochemistry Medicine Genetics Pharmacology Biotechnology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified unique molecular context improve treatment efficacy specific molecular interactions complex biological interactions attention synergy ), two benchmark datasets provides interpretable insights personalized cancer treatment causal attention model causal attention mechanism improvements allow casynergy cancer cell line causal attention cell line gene interactions attention module multimodal datasets interpretable prediction yet suffer tumor heterogeneity reliable way propose casynergy promising strategy prior approaches overcome resistance inadequate modeling challenging due casynergy offers casynergy introduces art models addresses limitations
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S2 Table - CASynergy: A causal attention model for interpretable prediction of cancer drug synergy

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