Meta-analysis of gene expression profiles in granulosa cells during folliculogenesis
Bibliographic record
Abstract
Folliculogenesis involves coordinated profound changes in different follicular compartments and significant modifications of their gene expression patterns, particularly in granulosa cells. Huge datasets have accumulated from the analyses of granulosa cell transcriptomic signatures in predefined physiological contexts using different technological platforms. However, no comprehensive overview of folliculogenesis is available. This would require integration of datasets from numerous individual studies. A prerequisite for such integration would be the use of comparable platforms and experimental conditions. The EmbryoGENE program was created to study bovine granulosa cell transcriptomics under different physiological conditions using the same platform. Based on the data thus generated so far, we present here an interactive web interface called GranulosaIMAGE (Integrative Meta-Analysis of Gene Expression), which provides dynamic expression profiles of any gene of interest and all isoforms thereof in granulosa cells at different stages of folliculogenesis. GranulosaIMAGE features two kinds of expression profiles: gene expression kinetics during bovine folliculogenesis from small (6 mm) to pre-ovulatory follicles under different hormonal and physiological conditions and expression profiles of granulosa cells of dominant follicles from post-partum cows in different metabolic states. This article provides selected examples of expression patterns along with suggestions for users to access and generate their own patterns using GranulosaIMAGE. The possibility of analysing gene expression dynamics during the late stages of folliculogenesis in a mono-ovulatory species such as bovine should provide a new and enriched perspective on ovarian physiology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".