ScholarDeck

Cohort studyBioinformatics (Oxford, England) · 2026

MoESurv: A Zero-Sample and Transferable Survival Prediction Framework for Rare Cancers Using Mixture of Experts.

Shuping Fang, Yuhang Wang, Mengyan Zhou … Hong Tian

Read paper

AI in MedicineCohort study

MoESurv framework improves survival prediction for rare cancers by 4 percentage points.

MoESurv: A Zero-Sample and Transferable Survival Prediction Framework for Rare Cancers Using Mixture of Experts.

Shuping Fang et al. · Bioinformatics (Oxford, England) · 2026

Background

MOTIVATION: Accurate survival prediction is crucial for personalized cancer treatment but remains challenging for rare cancers due to limited data.

4

Results

percentage points

the new method improved the accuracy of survival prediction for rare cancers

More results

We propose MoESurv, a zero-sample survival prediction framework that leverages a mix-ture-of-experts architecture to extract generalizable prognostic patterns from pan-cancer data.

The model integrates shared experts, cancer-specific experts, and routing experts within an autoencoder to disentangle common and type-specific survival features.

More results

Further-more, external validation across diverse populations and independent cohorts-including a Chinese glioma cohort (CGGA mRNAseq_693, C-index=0.7433), a rare GBM IDH-mutant subtype (C-index=0.8064), and the pan-cancer PCAWG cohort (C-index=0.7090)-demonstrated that MoESurv possesses the most robust predictive per-formance, highlighting its generalizability.

“

Conclusion

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Post

MoESurv: A Zero-Sample and Transferable Survival Prediction Framework for Rare Cancers Using Mixture of Experts.

MoESurv framework improves survival prediction for rare cancers by 4 percentage points.

Related papers