ArticleStatistics in medicine2020
Genome-wide association study-based deep learning for survival prediction.
Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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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
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.
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Who cites it
20 citing papers in PubMed.
- Empirical evaluation of variability and multi-institutional generalizability of deep learning survival models: application to renal cancer CT scans.Computers in biology and medicine · 2026Article
- Mini-batch Estimation for Deep Cox Models: Statistical Foundations and Practical Guidance.Journal of the American Statistical Association · 2026Article
- Estimation of the interpretable heterogeneous treatment effect with causal subgroup discovery in survival outcomes.Lifetime data analysis · 2026Article
- Enhanced temporal encoding-decoding for survival analysis of multimodal clinical data in smart healthcare.Visual computing for industry, biomedicine, and art · 2025Article
- Evaluating Meta-Learners to Analyze Treatment Heterogeneity in Survival Data: Application to Electronic Health Records of Pediatric Asthma Care in COVID-19 Pandemic.Statistics in medicine · 2025Article
- Neural Network for Correlated Survival Outcomes Using Frailty Model.Journal of data science : JDS · 2025Article
- A Meta-Learner Framework to Estimate Individualized Treatment Effects for Survival Outcomes.Journal of data science : JDS · 2024Article
- A penalized integrative deep neural network for variable selection among multiple omics datasets.Quantitative biology (Beijing, China) · 2024Article
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients.Cell reports methods · 2024Article
- Artificial intelligence for dementia genetics and omics.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Explainable artificial intelligence for omics data: a systematic mapping study.Briefings in bioinformatics · 2023Review
- PathExpSurv: pathway expansion for explainable survival analysis and disease gene discovery.BMC bioinformatics · 2023Article
- Neural network on interval-censored data with application to the prediction of Alzheimer's disease.Biometrics · 2023Article
- Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective.Current genomics · 2022Review
- Article
- Research on Pathogenic Hippocampal Voxel Detection in Alzheimer's Disease Using Clustering Genetic Random Forest.Frontiers in psychiatry · 2022Article
- Default risk prediction and feature extraction using a penalized deep neural network.Statistics and computing · 2022Article
- Neural Networks for Survival Prediction in Medicine Using Prognostic Factors: A Review and Critical Appraisal.Computational and mathematical methods in medicine · 2022Review
- A Simulation Study to Compare the Predictive Performance of Survival Neural Networks with Cox Models for Clinical Trial Data.Computational and mathematical methods in medicine · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Informative and accurate survival prediction with individualized dynamic risk profiles over time is critical for personalized disease prevention and clinical management. The massive genetic data, such as SNPs from genome-wide association studies (GWAS), together with well-characterized time-to-event phenotypes provide unprecedented opportunities for developing effective survival prediction models. Recent advances in deep learning have made extraordinary achievements in establishing powerful prediction models in the biomedical field. However, the applications of deep learning approaches in survival prediction are limited, especially with utilizing the wealthy GWAS data. Motivated by developing powerful prediction models for the progression of an eye disease, age-related macular degeneration (AMD), we develop and implement a multilayer deep neural network (DNN) survival model to effectively extract features and make accurate and interpretable predictions. Various simulation studies are performed to compare the prediction performance of the DNN survival model with several other machine learning-based survival models. Finally, using the GWAS data from two large-scale randomized clinical trials in AMD with over 7800 observations, we show that the DNN survival model not only outperforms several existing survival prediction models in terms of prediction accuracy (eg, c-index =0.76), but also successfully detects clinically meaningful risk subgroups by effectively learning the complex structures among genetic variants. Moreover, we obtain a subject-specific importance measure for each predictor from the DNN survival model, which provides valuable insights into the personalized early prevention and clinical management for this disease.
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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.