Pathway information on methylation analysis using deep neural network (PROMINENT): An interpretable deep learning method with pathway prior for phenotype prediction using gene-level DNA methylation
Year: 2025
Type: article
Abstract: BACKGROUND: DNA methylation is a key epigenetic marker that influences gene expression and phenotype regulation, and is affected by both genetic and environmental factors. Traditional linear regression methods such as elastic nets have been employed to assess the cumulative effects of multiple DNA methylation markers on phenotypes. However, these methods often fail to capture the complex nonlinear nature of the data. Recent deep learning approaches, such as MethylNet, have improved the prediction accuracy but lack interpretability and efficiency. FINDINGS: To address these limitations, we introduced Pathway Information on Methylation Analysis using a Deep Neural Network (PROMINENT), a novel interpretable deep learning method that integrates gene-level DNA methylation data with biological pathway information for phenotype prediction. PROMINENT enhances interpretability and prediction accuracy by incorporating gene- and pathway-level priors from databases such as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). It employs SHapley Additive exPlanations (SHAP) to prioritize significant genes and pathways. Evaluated across various datasets, childhood asthma, idiopathic pulmonary fibrosis (IPF), and first-episode psychosis (FEP)-PROMINENT consistently outperformed existing methods in terms of prediction accuracy and computational efficiency. PROMINENT also identified crucial genes and pathways involved in disease mechanisms. CONCLUSIONS: PROMINENT represents a significant advancement in leveraging DNA methylation data for phenotype prediction, offering both high accuracy and interpretability within reasonable computational time. This method holds promise for elucidating the epigenetic underpinnings of complex diseases and enhancing the utility of DNA methylation data in biomedical research.
Institution: University of Pittsburgh
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,480
Funders: National Science Foundation, University of Pittsburgh, National Institutes of Health, Medical Center, University of Pittsburgh, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of General Medical Sciences, Hillman Cancer Center, Medical Center, University of Pittsburgh
Awards: G1361707066, G3025648356, G3085519557, High-Throughput Computing for Genomics and Bioinformatics Research, MRI: Acquisition of Cutting-Edge GPU and MPI Nodes for the Interdisciplinary Pitt Center for Research Computing, G5734475935, G741073515, G7580304343, Inflammation Phenotypes in Pediatric Sepsis Induced Multiple Organ Failure Renewal, Identifying Genetic and Epigenetic Risk Factors Regulating Gene Expression for Childhood Asthma
OpenAlex ID: W4413805473