ArticleScientific reports2020
Collective effects of long-range DNA methylations predict gene expressions and estimate phenotypes in cancer.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 28 citations in OpenAlex.
- Unified imputation of missing data modalities and features in multi-omic data via shared representation learning.bioRxiv : the preprint server for biology · 2026Article
- Estimating in silico causal effects of DNA methylation on gene expression through genetic anchors in airway epithelium in asthma.Science advances · 2026Article
- A Region-Aware Structured Framework Improves Prediction of Gene Expression from DNA Methylation.Computational and structural biotechnology journal · 2026Article
- 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.Artificial intelligence in medicine · 2025Article
- Brain DNA Methylation Atlas of AppAging cell · 2025Article
- Exploring potential mechanisms of an African protective locus for Alzheimer's disease in APOEε4 carriers.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- DeepMethyGene: a deep-learning model to predict gene expression using DNA methylations.BMC bioinformatics · 2025Article
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- MOSES: a methylation-based gene association approach for unveiling environmentally regulated genes linked to a trait or disease.Clinical epigenetics · 2024Article
- Whole genome methylation sequencing in blood from persons with mild cognitive impairment and dementia due to Alzheimer's disease identifies cognitive status.bioRxiv : the preprint server for biology · 2024Article
- High-dimensional Ageome Representations of Biological Aging across Functional Modules.bioRxiv : the preprint server for biology · 2024Article
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- Whole genome methylation sequencing in blood identifies extensive differential DNA methylation in late-onset dementia due to Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Transcription factor expression repertoire basis for epigenetic and transcriptional subtypes of colorectal cancers.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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- Genome-wide methylomic regulation of multiscale gene networks in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Deep neural networks with knockoff features identify nonlinear causal relations and estimate effect sizes in complex biological systems.GigaScience · 2022Article
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Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA methylation of various genomic regions has been found to be associated with gene expression in diverse biological contexts. However, most genome-wide studies have focused on the effect of (1) methylation in cis, not in trans and (2) a single CpG, not the collective effects of multiple CpGs, on gene expression. In this study, we developed a statistical machine learning model, geneEXPLORE (gene expression prediction by long-range epigenetics), that quantifies the collective effects of both cis- and trans- methylations on gene expression. By applying geneEXPLORE to The Cancer Genome Atlas (TCGA) breast and 10 other types of cancer data, we found that most genes are associated with methylations of as much as 10 Mb from the promoters or more, and the long-range methylation explains 50% of the variation in gene expression on average, far greater than cis-methylation. geneEXPLORE outperforms competing methods such as BioMethyl and MethylXcan. Further, the predicted gene expressions could predict clinical phenotypes such as breast tumor status and estrogen receptor status (AUC = 0.999, 0.94 respectively) as accurately as the measured gene expression levels. These results suggest that geneEXPLORE provides a means for accurate imputation of gene expression, which can be further used to predict clinical phenotypes.
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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.