Gene Expression Analyses for Elucidating Mechanisms of Hormonal Action in Plants
Bibliographic record
Abstract
Analysis of large-scale gene expression data sets is proving to be a powerful tool for gene function prediction, cis-element discovery and hypothesis generation using Arabidopsis thaliana. Public initiatives led by the AtGenExpress Consortium and experiments conducted by individual researchers to document the transcriptome of Arabidopsis thaliana have led to a large numbers of data sets being made publicly available for data mining by so-called "electronic northerns", co-expression analysis and other means. Given that approximately 50% genes in Arabidopsis have no function ascribed to them by "traditional" homology searches, and that only around 10% of the genes have had their function confirmed in the laboratory, these analyses can accelerate the identification of potential gene function with a mouse-click. This chapter covers the use of data mining tools available at the Bio-Array Resource (www.bar.utoronto.ca) for hypothesis generation in the context of plant hormone biology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".