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Paper

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

New research · Internal Medicine

NPJ digital medicine · 1mo

AI / informaticsNPJ digital medicine· Jul 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 / informaticsQ1IF 18.0

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 … Mei Zhang

NPJ digital medicine · Jul 2026 · Q1 in General Medicine

Background

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

AUC = 0.939

Results

AUC = 0.939

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.

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Conclusion

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

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