GBD-DRP: Generalizable Bias-Disentangled Learning for Drug Response Prediction with Missing Modalities

摘要

Predicting the response of anticancer drugs for specific cancer cell lines remains an important challenge in cancer treatment, largely due to the diversity among cell lines. Existing machine learning methods usually perform well on cell lines seen during training, but their performance often drops when applied to new cell lines. Many approaches rely heavily on cell-line specific features, which limits their ability to generalize. At the same time, methods that focus mainly on generalization often fail to capture differences in drug response across distinct cellular environments. Some studies improve prediction accuracy by introducing additional and more detailed cell line features. However, these features are difficult to collect and are frequently missing, especially for newly studied cell lines. To address these issues, we propose a generalizable bias-disentangled framework for drug response prediction. Our method separates drug-related patterns that are shared across cell lines from representations that reflect cell-line specific environments. We further isolate real but non-generalizable biases that arise from noise or unobserved factors. This design helps preserve accurate predictions for specific cell lines while improving performance on unseen ones. In addition, the bias-related branch supports counterfactual consistency learning when some modalities are missing, allowing the model to handle incomplete data. Experimental results show that GBD-DRP achieves state-of-the-art performance, and additional tests on unseen cell lines and missing-modality settings indicate good generalization in practice.