ArticleNucleic acids research2023
Deep integrative models for large-scale human genomics.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
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Who cites it
16 citing papers in PubMed.
- Causal Transformer for Learning Embeddings from Structured Medical History Records and Multi-Source Data Integration for Complex Disease Risk Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Review
- Modeling nascent transcription from chromatin landscape and structure with CLASTER.Genome biology · 2026Article
- A deep ensemble encoder network method for improved polygenic risk score prediction.BioData mining · 2026Article
- Partially connected neural networks for complex trait prediction: application to human height.Human genetics · 2025Article
- Review
- Complex genetic effects linked to plasma protein abundance in the UK Biobank.Nature communications · 2025Article
- A survey on deep learning for polygenic risk scores.Briefings in bioinformatics · 2025Review
- Performance of deep-learning-based approaches to improve polygenic scores.Nature communications · 2025Article
- Deep learning-based polygenic scores enhance generalizability of psychiatric disorders prediction.medRxiv : the preprint server for health sciences · 2025Article
- Modeling gene interactions in polygenic prediction via geometric deep learning.Genome research · 2025Article
- Assessing polyomic risk to predict Alzheimer's disease using a machine learning model.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Article
- Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.NPJ systems biology and applications · 2024Article
- An assessment of the value of deep neural networks in genetic risk prediction for surgically relevant outcomes.PloS one · 2024Article
- A roadmap for multi-omics data integration using deep learning.Briefings in bioinformatics · 2022Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polygenic risk scores (PRSs) are expected to play a critical role in precision medicine. Currently, PRS predictors are generally based on linear models using summary statistics, and more recently individual-level data. However, these predictors mainly capture additive relationships and are limited in data modalities they can use. We developed a deep learning framework (EIR) for PRS prediction which includes a model, genome-local-net (GLN), specifically designed for large-scale genomics data. The framework supports multi-task learning, automatic integration of other clinical and biochemical data, and model explainability. When applied to individual-level data from the UK Biobank, the GLN model demonstrated a competitive performance compared to established neural network architectures, particularly for certain traits, showcasing its potential in modeling complex genetic relationships. Furthermore, the GLN model outperformed linear PRS methods for Type 1 Diabetes, likely due to modeling non-additive genetic effects and epistasis. This was supported by our identification of widespread non-additive genetic effects and epistasis in the context of T1D. Finally, we constructed PRS models that integrated genotype, blood, urine, and anthropometric data and found that this improved performance for 93% of the 290 diseases and disorders considered. EIR is available at https://github.com/arnor-sigurdsson/EIR.
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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.