| Dual-view jointly learning improves personalized drug synergy prediction |
| 論文作者 |
Li, XL; Shen, BH; Feng, FYM; Li, KS; Tang, ZX; Ma, LX; Li, H |
| 期刊/會(huì)議名稱 |
BIOINFORMATICS |
| 論文年度 |
2024 |
| 論文類別 |
|
| 摘要 |
Motivation: Accurate and robust estimation of the synergistic drug combination is important for medicine precision. Although some computational methods have been developed, some predictions are still unreliable especially for the cross-dataset predictions, due to the complex mechanism of drug combinations and heterogeneity of cancer samples. Results: We have proposed JointSyn that utilizes dual-view jointly learning to predict sample-specific effects of drug combination from drug and cell features. JointSyn outperforms existing state-of-the-art methods in predictive accuracy and robustness across various benchmarks. Each view of JointSyn captures drug synergy-related characteristics and makes complementary contributes to the final prediction of the drug combination. Moreover, JointSyn with fine-tuning improves its generalization ability to predict a novel drug combination or cancer sample using a small number of experimental measurements. We also used JointSyn to generate an estimated atlas of drug synergy for pan-cancer and explored the differential pattern among cancers. These results demonstrate the potential of JointSyn to predict drug synergy, supporting the development of personalized combinatorial therapies. |
| 期 |
10 |
| 卷 |
40 |
| 影響因子 |
4.4 |
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