Analysis and integration of microarray data of <i>Arabidopsis</i> mutants
Bibliographic record
Abstract
Zhou, D., Liu, R. and Xiong, S. 2014. Analysis and integration of microarray data of Arabidopsis mutants. Can. J. Plant Sci. 94: 235–243. Nowadays, high-throughput microarray data make it possible to study biological data on a large scale. It has successfully been applied to the gene function prediction in yeast, hypersensitive response in response to pathogen and human cancer. However, within the microarray data, there exists lots of unknown information which is worth mining. Based on mutants’ signature genes of Arabidopsis thaliana, we constructed a reference matrix including 267 pairs of subsets of differential reference profiles. We analyzed our data through expression profiles and connectivity map. Two notable results were detected by comparing every mutant in the matrix. Above all, the data mining procedure confirmed the biological relations not only between different stresses and glucose metabolism, but also stresses and MAPK signaling pathway among HSP90, PGM, VTE1, AXR4, SFR6, and SFR2 mutants. In addition, sfr6 might be involved in light cycle regulations, in accordance with the results of the overlap analysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".