AN INTEGRATED BIOINFORMATICS APPROACH TO THE DISCOVERY OF CIS -REGULATORY ELEMENTS INVOLVED IN PLANT GRAVITROPIC SIGNAL TRANSDUCTION
Bibliographic record
Abstract
Gravity is a common stimulus affecting plant growth and development, from seed germination to positioning of flowers for pollination and seeds for dispersal.Classic models of plant gravitropism have revolved around biophysical perception of the gravity stimulus and the effects of plant growth regulators on the growth response.Transcriptional regulation of the gravitropic mechanism has been largely ignored.The aim of this experiment is to identify putative regulatory functional elements, including transcription factor binding sites and cis -regulatory modules involved in gravitropic signal transduction.In this article, we detailed a strategy to identify putative cis -regulatory elements by analyzing gene expression data from microarray experiments.Genes involved in the gravitropic perceptionresponse pathway were identified based on their changes in expression level after gravity stimulation.Genes were clustered according to their expression patterns (transcriptional regulation profiles), and gene promoter were analyzed using genomics regulatory analysis software to identify candidate cis -regulatory elements and cis -regulatory modules.Analysis of the microarray data indicated that 154 genes were involved in the gravitropic response.The genes were grouped into 9 clusters based on expression profile similarities.An analysis of the promoters of the 154 genes resulted in the identification of 32 putative regulatory elements and 55 putative regulatory modules.Some of the elements are associated with individual clusters and other elements are associated with multiple clusters, potentially indicating elements involved in specific and in general gravitropic response processes, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".