ArticleGenetic epidemiology2023
Gene-environment interaction analysis via deep learning.
Article in Genetic epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- Tailoring AI and ML models for genotype-by-environment prediction leveraging environmental covariates: A European rye example.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network-Based Cox Model.Statistics in medicine · 2026Article
- Impact of prenatal environmental exposure on offspring neurodevelopment and susceptibility to neurodegenerative diseases: mechanisms and perspectives.Frontiers in public health · 2026Review
- Intra- and inter-multi-omics interaction analysis using deep learning.Bioinformatics advances · 2026Article
- Ordinal Sparse Neural Networks for Modeling Gene- and Imaging-Environment Interactions.Statistics in medicine · 2025Article
- Joint modeling of mixed outcomes using a rank-based sparse neural network.Journal of biomedical informatics · 2025Article
- GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.Bioinformatics (Oxford, England) · 2025Article
- High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025Article
- Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues.Human genomics · 2025Review
- Gene-environment interactions in human health.Nature reviews. Genetics · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Gene-environment (G-E) interaction analysis plays an important role in studying complex diseases. Extensive methodological research has been conducted on G-E interaction analysis, and the existing methods are mostly based on regression techniques. In many fields including biomedicine and omics, it has been increasingly recognized that deep learning may outperform regression with its unique flexibility (e.g., in accommodating unspecified nonlinear effects) and superior prediction performance. However, there has been a lack of development in deep learning for G-E interaction analysis. In this article, we fill this important knowledge gap and develop a new analysis approach based on deep neural network in conjunction with penalization. The proposed approach can simultaneously conduct model estimation and selection (of important main G effects and G-E interactions), while uniquely respecting the "main effects, interactions" variable selection hierarchy. Simulation shows that it has superior prediction and feature selection performance. The analysis of data on lung adenocarcinoma and skin cutaneous melanoma overall survival further establishes its practical utility. Overall, this study can advance G-E interaction analysis by delivering a powerful new analysis approach based on modern deep learning.
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.