ArticlePlant biotechnology journal2020
Learning from methylomes: epigenomic correlates of Populus balsamifera traits based on deep learning models of natural DNA methylation.
Article in Plant biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- AI-Augmented Multi-Omics for Abiotic Stress Responses: A New Frontier in Plant Hormone Systems Biology.Plants (Basel, Switzerland) · 2026Review
- Artificial Intelligence Methods in Forest Biotechnology: Current Status and Future Prospects.International journal of molecular sciences · 2026Review
- Gaining insights into epigenetic memories through artificial intelligence and omics science in plants.Journal of integrative plant biology · 2025Review
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Roles for epigenetics in wood formation and stress response intrees-from basic biology to forest management.Frontiers in epigenetics and epigenomics · 2025Review
- TPS5 and TOR signaling components are determinants ofFrontiers in plant science · 2025Article
- EarlySensors (Basel, Switzerland) · 2024Article
- A review of plant epigenetics through the lens of almond.The plant genome · 2023Review
- Machine learning: its challenges and opportunities in plant system biology.Applied microbiology and biotechnology · 2022Review
- Plant Genotype to Phenotype Prediction Using Machine Learning.Frontiers in genetics · 2022Review
- Review
- Learning from methylomes: epigenomic correlates of Populus balsamifera traits based on deep learning models of natural DNA methylation.Plant biotechnology journal · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epigenomes have remarkable potential for the estimation of plant traits. This study tested the hypothesis that natural variation in DNA methylation can be used to estimate industrially important traits in a genetically diverse population of Populus balsamifera L. (balsam poplar) trees grown at two common garden sites. Statistical learning experiments enabled by deep learning models revealed that plant traits in novel genotypes can be modelled transparently using small numbers of methylated DNA predictors. Using this approach, tissue type, a nonheritable attribute, from which DNA methylomes were derived was assigned, and provenance, a purely heritable trait and an element of population structure, was determined. Significant proportions of phenotypic variance in quantitative wood traits, including total biomass (57.5%), wood density (40.9%), soluble lignin (25.3%) and cell wall carbohydrate (mannose: 44.8%) contents, were also explained from natural variation in DNA methylation. Modelling plant traits using DNA methylation can capture tissue-specific epigenetic mechanisms underlying plant phenotypes in natural environments. DNA methylation-based models offer new insight into natural epigenetic influence on plants and can be used as a strategy to validate the identity, provenance or quality of agroforestry products.
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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.