ArticlePLoS computational biology2022
Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Interpreting artificial neural networks to detect genome-wide association signals for complex traits.NAR genomics and bioinformatics · 2026Article
- Evaluating genetic-based disease prediction approaches through simulation.Human genetics · 2026Article
- Article
- A survey on deep learning for polygenic risk scores.Briefings in bioinformatics · 2025Review
- Detecting genetic interactions with visible neural networks.Communications biology · 2025Article
- Auto-branch multi-task learning for simultaneous prediction of multiple correlated traits associated with Alzheimer's disease.Frontiers in genetics · 2025Article
- Genetic risk predictions using deep learning models with summary data.Frontiers in bioinformatics · 2025Article
- Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data.Briefings in functional genomics · 2024Article
- Community-engaged artificial intelligence research: A scoping review.PLOS digital health · 2024Article
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.Plant communications · 2024Article
- The impact of Bayesian optimization on feature selection.Scientific reports · 2024Article
- Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations.ACS omega · 2024Review
- Deep learning captures the effect of epistasis in multifactorial diseases.Frontiers in medicine · 2024Article
- Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.Frontiers in cellular and infection microbiology · 2024Review
- Applications for Deep Learning in Epilepsy Genetic Research.International journal of molecular sciences · 2023Review
- Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.Sensors (Basel, Switzerland) · 2023Article
- Functional Neural Networks for High-Dimensional Genetic Data Analysis.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Building an accurate disease risk prediction model is an essential step in the modern quest for precision medicine. While high-dimensional genomic data provides valuable data resources for the investigations of disease risk, their huge amount of noise and complex relationships between predictors and outcomes have brought tremendous analytical challenges. Deep learning model is the state-of-the-art methods for many prediction tasks, and it is a promising framework for the analysis of genomic data. However, deep learning models generally suffer from the curse of dimensionality and the lack of biological interpretability, both of which have greatly limited their applications. In this work, we have developed a deep neural network (DNN) based prediction modeling framework. We first proposed a group-wise feature importance score for feature selection, where genes harboring genetic variants with both linear and non-linear effects are efficiently detected. We then designed an explainable transfer-learning based DNN method, which can directly incorporate information from feature selection and accurately capture complex predictive effects. The proposed DNN-framework is biologically interpretable, as it is built based on the selected predictive genes. It is also computationally efficient and can be applied to genome-wide data. Through extensive simulations and real data analyses, we have demonstrated that our proposed method can not only efficiently detect predictive features, but also accurately predict disease risk, as compared to many existing methods.
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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.