ArticlePLoS computational biology2018
Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 166 papers, 9 of them syntheses that pooled it.
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Who cites it
166 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- A Systematic Review on Machine Learning Techniques for Survival Analysis in Cancer.Cancer medicine · 2025Pooled it
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- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide.Frontiers in bioengineering and biotechnology · 2023Pooled it
- Pooled it
- Deep learning techniques for cancer classification using microarray gene expression data.Frontiers in physiology · 2022Pooled it
- Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review.Frontiers in oral health · 2021Pooled it
- Systematic Analysis of microRNA Biomarkers for Diagnosis, Prognosis, and Therapy in Patients With Clear Cell Renal Cell Carcinoma.Frontiers in oncology · 2020Pooled it
- TMO-Net+: An Enhanced Tumor Multi-Omics Pre-Trained Network for Multi-Task Learning in Oncology.Genes · 2026Article
- Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions.Journal of the Egyptian National Cancer Institute · 2026Review
- MoESurv: a zero-sample and transferable survival prediction framework for rare cancers using mixture of experts.Bioinformatics (Oxford, England) · 2026Article
- Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation.Bioinformatics (Oxford, England) · 2026Article
- Article
- Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.bioRxiv : the preprint server for biology · 2026Article
- SleepJEPA: Learning the latent world of sleep with at-home sleep data to estimate disease risk.medRxiv : the preprint server for health sciences · 2026Article
- Mini-batch Estimation for Deep Cox Models: Statistical Foundations and Practical Guidance.Journal of the American Statistical Association · 2026Article
- A comparative analysis of data-driven models for breast cancer survival prediction.Scientific reports · 2026Article
- Discrete-time neural Markov models.BMC medical research methodology · 2026Article
- scSurv: a deep generative model for single-cell survival analysis.Bioinformatics (Oxford, England) · 2026Article
106 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet.
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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.