Machine learning analyses of methylation profiles uncovers tissue‐specific gene expression patterns in wheat
Bibliographic record
Abstract
Abstract DNA methylation is a mechanism of epigenetic modification in eukaryotic organisms. Generally, methylation within genes promoter inhibits regulatory protein binding and represses transcription, whereas gene body methylation is associated with actively transcribed genes. However, it remains unclear whether there is interaction between methylation levels across genic regions and which site has the biggest impact on gene regulation. We investigated and used the methylation patterns of the bread wheat cultivar Chinese Spring to uncover differentially expressed genes (DEGs) between roots and leaves, using six machine learning algorithms and a deep neural network. As anticipated, genes with higher expression in leaves were mainly involved in photosynthesis and pigment biosynthesis processes whereas genes that were not differentially expressed between roots and leaves were involved in protein processes and membrane structures. Methylation occurred preponderantly (60%) in the CG context, whereas 35 and 5% of methylation occurred in CHG and CHH contexts, respectively. Methylation levels were highly correlated (r = 0.7 to 0.9) between all genic regions, except within the promoter (r = 0.4 to 0.5). Machine learning models gave a high (0.81) prediction accuracy of DEGs. There was a strong correlation (p‐value = 9.20×10 −10 ) between all features and gene expression, suggesting that methylation across all genic regions contribute to gene regulation. However, the methylation of the promoter, the CDS and the exon in CG context was the most impactful. Our study provides more insights into the interplay between DNA methylation and gene expression and paves the way for identifying tissue‐specific genes using methylation profiles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".