ArticleBMC medical genomics2020
Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations.
Article in BMC medical genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 4 of them syntheses that pooled it.
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Who cites it
48 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- A Systematic Review on Machine Learning Techniques for Survival Analysis in Cancer.Cancer medicine · 2025Pooled it
- Immune-related pan-cancer gene expression signatures of patient survival revealed by NanoString-based analyses.PloS one · 2023Pooled it
- Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide.Frontiers in bioengineering and biotechnology · 2023Pooled it
- Deep learning techniques for cancer classification using microarray gene expression data.Frontiers in physiology · 2022Pooled it
- Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation.Bioinformatics (Oxford, England) · 2026Article
- MANet: a multimodal attention convolutional neural network for brain tumor classification.Scientific reports · 2026Article
- scSurvival: Single-Cell Survival Analysis of Clinical Cancer Cohort Data at Cellular Resolution.Cancer discovery · 2026Article
- scSurv: a deep generative model for single-cell survival analysis.Bioinformatics (Oxford, England) · 2026Article
- Leveraging single-cell foundation models for accurate survival outcome prediction.Bioinformatics advances · 2026Article
- Transformer-based representation learning for robust gene expression modeling and cancer prognosis.Scientific reports · 2025Article
- Fatty acid-binding protein 4 as a biomarker for colon adenocarcinoma risk and prognosis: Challenges and future directions.World journal of gastrointestinal oncology · 2025Article
- Article
- Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis.Briefings in bioinformatics · 2025Article
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- Spatial transcriptome reveals histology-correlated immune signature learnt by deep learning attention mechanism on H&E-stained images for ovarian cancer prognosis.Journal of translational medicine · 2025Article
- Classification and diagnosis of cervical lesions based on colposcopy images using deep fully convolutional networks: A man-machine comparison cohort study.Fundamental research · 2025Article
- Exploiting common patterns in diverse cancer types via multi-task learning.NPJ precision oncology · 2024Article
- Artificial intelligence: illuminating the depths of the tumor microenvironment.Journal of translational medicine · 2024Review
- Robust evaluation of deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data.Scientific reports · 2024Article
- Case-Base Neural Network: Survival analysis with time-varying, higher-order interactions.Machine learning with applications · 2024Article
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14 authors.
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Abstract
backgroundRecent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients.
methodsIn this study, a broad analysis was performed on TCGA cancers using a variety of Deep Learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances.
resultsAll models show competitive results across 12 cancer types. The last hidden layers of the Deep Learning approaches are lower dimensional representations of the input data that can be used for feature reduction and visualization. Furthermore, the prognosis performances reveal a negative correlation between model accuracy, overall survival time statistics, and tumor mutation burden (TMB), suggesting an association among overall survival time, TMB, and prognosis prediction accuracy.
conclusionsDeep Learning based algorithms demonstrate superior performances than traditional machine learning based models. The cancer prognosis results measured in concordance index are indistinguishable across models while are highly variable across cancers. These findings shedding some light into the relationships between patient characteristics and survival learnability on a pan-cancer level.
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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.