ArticleBioinformatics (Oxford, England)2024
Deep5hmC: predicting genome-wide 5-hydroxymethylcytosine landscape via a multimodal deep learning model.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Organoid intelligence: a promising paradigm for autism spectrum disorder research.Molecular psychiatry · 2026Review
- MeDiCNet: Integrating Multi-scale Dynamic Convolution and Enhanced Position-Aware Transformer for DNA Methylation Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- iDNA-DAPHA: a generic framework for methylation prediction via domain-adaptive pretraining and hierarchical attention.Briefings in bioinformatics · 2025Article
- Cell identity and 5-hydroxymethylcytosine.Epigenetics & chromatin · 2025Review
- DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models.Frontiers in medicine · 2025Review
- In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder.bioRxiv : the preprint server for biology · 2024Article
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Abstract
motivation5-Hydroxymethylcytosine (5hmC), a crucial epigenetic mark with a significant role in regulating tissue-specific gene expression, is essential for understanding the dynamic functions of the human genome. Despite its importance, predicting 5hmC modification across the genome remains a challenging task, especially when considering the complex interplay between DNA sequences and various epigenetic factors such as histone modifications and chromatin accessibility.
resultsUsing tissue-specific 5hmC sequencing data, we introduce Deep5hmC, a multimodal deep learning framework that integrates both the DNA sequence and epigenetic features such as histone modification and chromatin accessibility to predict genome-wide 5hmC modification. The multimodal design of Deep5hmC demonstrates remarkable improvement in predicting both qualitative and quantitative 5hmC modification compared to unimodal versions of Deep5hmC and state-of-the-art machine learning methods. This improvement is demonstrated through benchmarking on a comprehensive set of 5hmC sequencing data collected at four developmental stages during forebrain organoid development and across 17 human tissues. Compared to DeepSEA and random forest, Deep5hmC achieves close to 4% and 17% improvement of Area Under the Receiver Operating Characteristic (AUROC) across four forebrain developmental stages, and 6% and 27% across 17 human tissues for predicting binary 5hmC modification sites; and 8% and 22% improvement of Spearman correlation coefficient across four forebrain developmental stages, and 17% and 30% across 17 human tissues for predicting continuous 5hmC modification. Notably, Deep5hmC showcases its practical utility by accurately predicting gene expression and identifying differentially hydroxymethylated regions (DhMRs) in a case-control study of Alzheimer's disease (AD). Deep5hmC significantly improves our understanding of tissue-specific gene regulation and facilitates the development of new biomarkers for complex diseases. AVAILABILITY AND IMPLEMENTATION: Deep5hmC is available via https://github.com/lichen-lab/Deep5hmC.
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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 Socratic graph.