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

Clicks: 7
ID: 322125
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #154 of 821 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 821 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Motivation Accurate survival prediction is crucial for personalized cancer treatment but remains challenging for rare cancers due to limited data. Most deep learning models require large training datasets, which are unavailable for rare cancer types, creating a significant clini-cal bottleneck. 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. Evaluated on seven rare TCGA cancer types, MoESurv achieved state-of-the-art performance, improving the average C-index by 4 percentage points over the best baseline. 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. MoESurv also effectively stratified high- and low-risk patient groups and identified potential survival-associated genes, demonstrating both clinical utility and biological interpretability. Availability The code is freely available at https://github.com/HuaYC666/MoESurv and https://zenodo.org/records/20785500. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7170068522 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shuping Fang, Yue Wang, M Zhou, Yanting Shao, Zhenghao Jiang, Yitong Guo, Tao Jiang, Wang Feng, Jingya Fang, Tian Hong
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag549
URL
https://doi.org/10.1093/bioinformatics/btag549
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.