Fused: Cross-Domain Integration Of Foundation Models For Cancer Drug Response Prediction
Rössner, T.; Balke, J.; Tang, M.
Show abstract
AO_SCPLOWBSTRACTC_SCPLOWAI-driven methods for predicting drug responses hold promise for advancing personalized cancer therapy, but cancer heterogeneity and the high cost of data generation pose substantial challenges. Here we explore the transfer learning capability and introduce FUSED (Fusion of Foundation Model Embeddings for Drug Response Prediction), a novel architecture for cross-domain foundation model (FM) integration. By systematically benchmark FMs across two domains - molecular FM for drugs and single-cell FM for cell lines, we demonstrate that integrating single-cell FMs substantially reduces the number of input features required for cell line representation. Among FMs, Molformer significantly outperforms ChemBERTa, and scGPT surpasses scFoundation in predictive accuracy and training stability. Moreover, integrating single-cell FMs improves performance in both drug-known and leave-one-drug-out scenarios. These findings highlight the potential of cross-domain FM integration for more efficient and robust drug response prediction.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Generating hard-to-obtain information from easy-to-obtain information: applications in drug discovery and clinical inference 96%
- Chemical-induced Gene Expression Ranking and its Application to Pancreatic Cancer Drug Repurposing 94%
- Federated Learning for multi-omics: a performance evaluation in Parkinson's disease 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.