ArticleNature communications2023
Getting personal with epigenetics: towards individual-specific epigenomic imputation with machine learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- Hybrid CNN and multi-head attention model for analyzing epigenetic mechanisms and gene expression across fungal phylogenetic distances.iScience · 2026Article
- Epigenetic editing approaches maturity: AI-driven precision design, delivery innovation, and the road to clinical translation.Clinical epigenetics · 2026Review
- Towards personalized epigenomics: learning shared chromatin landscapes and joint de-noising of histone modification assays.NAR genomics and bioinformatics · 2025Article
- GECSI: Large-scale chromatin state imputation from gene expression.bioRxiv : the preprint server for biology · 2025Article
- Epigenetics and individuality: from concepts to causality across timescales.Nature reviews. Genetics · 2025Review
- Epigenetic dysregulation in glioblastoma: potential pathways to precision medicine.Neurogenetics · 2024Review
- Hypomethylation of theInternational journal of molecular sciences · 2024Article
- Bottom-up data integration in polymer models of chromatin organization.Biophysical journal · 2024Article
- Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.International journal of molecular sciences · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Epigenetic modifications are dynamic mechanisms involved in the regulation of gene expression. Unlike the DNA sequence, epigenetic patterns vary not only between individuals, but also between different cell types within an individual. Environmental factors, somatic mutations and ageing contribute to epigenetic changes that may constitute early hallmarks or causal factors of disease. Epigenetic modifications are reversible and thus promising therapeutic targets for precision medicine. However, mapping efforts to determine an individual's cell-type-specific epigenome are constrained by experimental costs and tissue accessibility. To address these challenges, we developed eDICE, an attention-based deep learning model that is trained to impute missing epigenomic tracks by conditioning on observed tracks. Using a recently published set of epigenomes from four individual donors, we show that transfer learning across individuals allows eDICE to successfully predict individual-specific epigenetic variation even in tissues that are unmapped in a given donor. These results highlight the potential of machine learning-based imputation methods to advance personalized epigenomics.
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What OpenQuestion holds
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.