ArticleBMC bioinformatics2025
DeepMethyGene: a deep-learning model to predict gene expression using DNA methylations.
Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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Who cites it
3 citing papers in PubMed.
- Epigenetic research methods and animal models for intervertebral disc degeneration (Review).Molecular medicine reports · 2026Review
- A Region-Aware Structured Framework Improves Prediction of Gene Expression from DNA Methylation.Computational and structural biotechnology journal · 2026Article
- MTMixG-Net: mixture of Transformer and Mamba network with a dual-path gating mechanism for plant gene expression prediction.Frontiers in plant science · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Gene expression is the basis for cells to achieve various functions, while DNA methylation constitutes a critical epigenetic mechanism governing gene expression regulation. Here we propose DeepMethyGene, an adaptive recursive convolutional neural network model based on ResNet that predicts gene expression using DNA methylation information. Our model transforms methylation Beta values to M values for Gaussian distributed data optimization, dynamically adjusts the output channels according to input dimension, and implements residual blocks to mitigate the problem of gradient vanishing when training very deep networks. Benchmarking against the state-of-the-art geneEXPLORE model (R
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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.