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New research · Oncology
NPJ digital medicine · 1d
AI / informaticsNPJ digital medicine · 2026

Multi-omics fusion with machine learning enables robust prediction of treatment response in ovarian cancer for precision population health.

Jie Chen, Tianshi Mao, Yu Yang … Mei Zhang
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OncologyAI / informatics

Multi-omics model accurately predicts ovarian cancer treatment response.

Multi-omics fusion with machine learning enables robust prediction of treatment response in ovarian cancer for precision population health.

Jie Chen et al. · NPJ digital medicine · 2026
Background

Inter-patient heterogeneity complicates predicting treatment response in ovarian cancer (OC).

AUC = 0.939
Results
This shows the model's excellent ability to predict ovarian cancer treatment response
More results

Nevertheless, the multi-omics framework yielded superior balance across accuracy and F1 score.

SHAP analysis identified key determinants of treatment response, including CLEC2A, MYH4, and methylation of SYT12_1, with functional enrichment implicating immune regulation, metabolic pathways, and drug resistance signaling.

More results

Experimental validation confirmed six hub genes (CASP8, AQP8, CAV1, FN1, CREB1, KDR), exhibiting expression patterns associated with drug resistance, immune regulation, and prognosis.

“
Conclusion

This multi-omics machine learning model enables robust, interpretable prediction, uncovering molecular signatures for therapeutic stratification and precision oncology in OC.

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