ArticleBioinformatics (Oxford, England)2020
Improved survival analysis by learning shared genomic information from pan-cancer data.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 81 citations in OpenAlex.
- Immune-related pan-cancer gene expression signatures of patient survival revealed by NanoString-based analyses.PloS one · 2023Pooled it
- MoESurv: a zero-sample and transferable survival prediction framework for rare cancers using mixture of experts.Bioinformatics (Oxford, England) · 2026Article
- SurvGRN: a multi-feature fusion framework for bladder cancer survival prediction.International journal of surgery (London, England) · 2026Article
- In silico generation of gene expression profiles using diffusion models.BMC bioinformatics · 2026Article
- DySurv: dynamic deep learning model for survival analysis with conditional variational inference.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- CoFormerSurv: Collaborative transformer for multi-omics survival analysis.PLoS computational biology · 2026Article
- Leveraging AI and transfer learning to enhance out-of-hospital cardiac arrest outcome prediction in diverse setting.NPJ digital medicine · 2025Article
- BRCAGenie: A machine learning-driven 43-gene polygenic risk score model for precision prediction of breast cancer survival.Journal of translational medicine · 2025Article
- Transformer-based representation learning for robust gene expression modeling and cancer prognosis.Scientific reports · 2025Article
- Cancer survival prediction based on soft-label guided contrastive learning and global feature fusion.Bioinformatics (Oxford, England) · 2025Article
- CoxKAN: Kolmogorov-Arnold networks for interpretable, high-performance survival analysis.Bioinformatics (Oxford, England) · 2025Article
- Novel cancer subtyping method guided by tumor-normal sample in latent space of transcriptomic variational autoencoder.Scientific reports · 2025Article
- Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.Genomics, proteomics & bioinformatics · 2025Review
- Federated transfer learning with differential privacy for multi-omics survival analysis.Briefings in bioinformatics · 2025Article
- Deep profiling of gene expression across 18 human cancers.Nature biomedical engineering · 2025Article
- Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis.Health data science · 2025Review
- MMOSurv: meta-learning for few-shot survival analysis with multi-omics data.Bioinformatics (Oxford, England) · 2024Article
- Learning to Train and to Explain a Deep Survival Model with Large-Scale Ovarian Cancer Transcriptomic Data.Biomedicines · 2024Article
- A deep profile of gene expression across 18 human cancers.bioRxiv : the preprint server for biology · 2024Article
- Deep contrastive learning for predicting cancer prognosis using gene expression values.Briefings in bioinformatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationRecent advances in deep learning have offered solutions to many biomedical tasks. However, there remains a challenge in applying deep learning to survival analysis using human cancer transcriptome data. As the number of genes, the input variables of survival model, is larger than the amount of available cancer patient samples, deep-learning models are prone to overfitting. To address the issue, we introduce a new deep-learning architecture called VAECox. VAECox uses transfer learning and fine tuning.
resultsWe pre-trained a variational autoencoder on all RNA-seq data in 20 TCGA datasets and transferred the trained weights to our survival prediction model. Then we fine-tuned the transferred weights during training the survival model on each dataset. Results show that our model outperformed other previous models such as Cox Proportional Hazard with LASSO and ridge penalty and Cox-nnet on the 7 of 10 TCGA datasets in terms of C-index. The results signify that the transferred information obtained from entire cancer transcriptome data helped our survival prediction model reduce overfitting and show robust performance in unseen cancer patient samples. AVAILABILITY AND IMPLEMENTATION: Our implementation of VAECox is available at https://github.com/dmis-lab/VAECox. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.