The identification of candidate radio marker genes using a coexpression network analysis in gamma‐irradiated rice
Bibliographic record
Abstract
Plant physiological and biochemical processes are significantly affected by gamma irradiation stress. In addition, gamma-ray (GA) differentially affects gene expression across the whole genome. In this study, we identified radio marker genes (RMGs) responding only to GA stress compared with six abiotic stresses (chilling, cold, anoxia, heat, drought and salt) in rice. To analyze the expression patterns of differentially expressed genes (DEGs) in gamma-irradiated rice plants against six abiotic stresses, we conducted a hierarchical clustering analysis by using a complete linkage algorithm. The up- and downregulated DEGs were observed against six abiotic stresses in three and four clusters among a total of 31 clusters, respectively. The common gene ontology functions of upregulated DEGs in clusters 9 and 19 are associated with oxidative stress. In a Pearson's correlation coefficient analysis, GA stress showed highly negative correlation with salt stress. On the basis of specific data about the upregulated DEGs, we identified the 40 candidate RMGs that are induced by gamma irradiation. These candidate RMGs, except two genes, were more highly induced in rice roots than in other tissues. In addition, we obtained other 38 root-induced genes by using a coexpression network analysis of the specific upregulated candidate RMGs in an ARACNE algorithm. Among these genes, we selected 16 RMGs and 11 genes coexpressed with three RMGs to validate coexpression network results. RT-PCR assay confirmed that these genes were highly upregulated in GA treatment. All 76 genes (38 root-induced genes and 38 candidate RMGs) might be useful for the detection of GA sensitivity in rice roots.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".