Gene Set Databases
Bibliographic record
Abstract
Gene set analysis is a well-established approach for analyzing high-throughput gene expression data. The choice of gene set database used for gene set analysis may affect the outcome of the analysis. Therefore, understanding characteristics of these databases is vital to the success of gene set analysis. Due to the sheer size of the gene set databases, a comprehensive qualitative evaluation of them is impractical. In this paper, we quantitatively study several well-established gene set databases. We propose and use a quantitative measure for assessing the similarity between gene set databases. Also, we introduce presence score, for quantifying the degree to which a given gene is represented in a database, and permeability score, for quantifying the degree to which genes in a given list co-occur in the gene sets of a database. A maximum achievable coverage score is defined based on the permeability score. Using the maximum achievable coverage score, we propose a methodology to statistically determine whether a phenotype of interest is well-represented in a given database. To study the effect of the choice of gene set database on the result of gene set analysis and show the utility of the maximum achievable coverage score, we conduct an experiment using two widely used gene set analysis methods and three expression datasets. The results suggest that the choice of gene set database might profoundly affect the outcome of the analysis. Also, our findings show that the permeability score and maximum achievable coverage can be used to guide the selection of an appropriate gene set database for a given study.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.050 |
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".