GEMS (Gene Expression Metasignatures), a Web Resource for Querying Meta-analysis of Expression Microarray Datasets: 17β-Estradiol in MCF-7 Cells
Bibliographic record
Abstract
With large amounts of public expression microrray data being generated by multiple laboratories, it is a significant task for the bench researcher to routinely identify available datasets, and then to evaluate the collective evidence across these datasets for regulation of a specific gene in a given system. 17beta-Estradiol stimulation of MCF-7 cells is a widely used model in the growth of breast cancer. Although myriad independent studies have profiled the global effects of this hormone on gene expression in these cells, disparate experimental variables and the limited power of the individual studies have combined to restrict the agreement between them as to the specific gene expression signature elicited by this hormone. To address these issues, we have developed a freely accessible Web resource, Gene Expression MetaSignatures (GEMS) that provides the user a consensus for each gene in the system. We conducted a weighted meta-analysis encompassing over 13,000 genes across 10 independent published datasets addressing the effect of 17beta-estradiol on MCF-7 cells at early (3-4 hours) and late (24 hours) time points. In a literature survey of 58 genes previously shown to be regulated by 17beta-estradiol in MCF-7 cells, the meta-analysis combined the statistical power of the underlying datasets to call regulation of these genes with nearly 85% accuracy (false discovery rate-corrected P < 0.05). We anticipate that with future expression microarray dataset contributions from investigators, GEMS will evolve into an important resource for the cancer and nuclear receptor signaling communities.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".