Dual-view jointly learning improves personalized drug synergy prediction.

ABSTRACT:

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,

SUBMITTER: Li X 

PROVIDER: S-EPMC11524890 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature