Analysis of Microarray Data of Rice EPAGRI 108 under Excess Fe Stress
Bibliographic record
Abstract
Iron is one of the essential micronutrients to rice, but the accumulation of excessive ferrous salt in soil can cause toxicity. In this study, bioinformatics method was used to mine the data of Affymetrix rice gene expression microarray to study the differentially expressed genes of rice germplasm EPAGRI 108 under control and excess Fe conditions. The results revealed that 407 genes with more than two fold difference were identified. Compared to the control group, 330 genes were up regulated and 77 genes were down regulated under excess Fe stress. Gene Ontology and pathway analysis revealed that these differentially expressed genes were mainly involved in the biological processes such as oxidoreductase activity, glucose metabolism, amino acid metabolism, etc. Through these data analysis, we preliminarily explored the gene expression patterns of rice under excess Fe conditions, and provided a theoretical basis for further investgating the molecular mechanism tolerating for rice tolerance to Fe toxicity.
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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) | 0.001 | 0.000 |
| 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) | 0.001 | 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".