ArticleBioinformatics advances2023
Learning from small medical data-robust semi-supervised cancer prognosis classifier with Bayesian variational autoencoder.
Article in Bioinformatics advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
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Who cites it
5 citing papers in PubMed.
- Self-supervised representation learning on gene expression data.Bioinformatics (Oxford, England) · 2025Article
- Deep learning enhances precision diagnosis and treatment of non-small cell lung cancer: future prospects.Translational lung cancer research · 2025Review
- Role of Generative Artificial Intelligence in Personalized Medicine: A Systematic Review.Cureus · 2025Review
- Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients.Cell reports methods · 2024Article
- A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment.Frontiers in genetics · 2023Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Cancer is one of the world's leading mortality causes, and its prognosis is hard to predict due to complicated biological interactions among heterogeneous data types. Numerous challenges, such as censorship, high dimensionality and small sample size, prevent researchers from using deep learning models for precise prediction. Results: We propose a robust Semi-supervised Cancer prognosis classifier with bAyesian variational autoeNcoder ( Availability and implementation: The source codes reproducing the main results are available on GitHub: https://gitfront.io/r/user-4316673/36e8714573f3fbfa0b24690af5d1a9d5ca159cf4/scan/. Supplementary information: Supplementary data are available at
Identifiers
What OpenQuestion holds
Registered trials
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