Consensus by Democracy. Using Meta-Analyses of Microarray and Genomic Data to Model the Cold Acclimation Signaling Pathway in Arabidopsis
Bibliographic record
Abstract
The whole-genome response of Arabidopsis (Arabidopsis thaliana) exposed to different types and durations of abiotic stress has now been described by a wealth of publicly available microarray data. When combined with studies of how gene expression is affected in mutant and transgenic Arabidopsis with altered ability to transduce the low temperature signal, these data can be used to test the interactions between various low temperature-associated transcription factors and their regulons. We quantized a collection of Affymetrix microarray data so that each gene in a particular regulon could vote on whether a cis-element found in its promoter conferred induction (+1), repression (-1), or no transcriptional change (0) during cold stress. By statistically comparing these election results with the voting behavior of all genes on the same gene chip, we verified the bioactivity of novel cis-elements and defined whether they were inductive or repressive. Using in silico mutagenesis we identified functional binding consensus variants for the transcription factors studied. Our results suggest that the previously identified ICEr1 (induction of CBF expression region 1) consensus does not correlate with cold gene induction, while the ICEr3/ICEr4 consensuses identified using our algorithms are present in regulons of genes that were induced coordinate with observed ICE1 transcript accumulation and temporally preceding genes containing the dehydration response element. Statistical analysis of overlap and cis-element enrichment in the ICE1, CBF2, ZAT12, HOS9, and PHYA regulons enabled us to construct a regulatory network supported by multiple lines of evidence that can be used for future hypothesis testing.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".