Editorial: Advancement in Gene Set Analysis: Gaining Insight From High-Throughput Data
Bibliographic record
Abstract
the interactions between the genes.Various categories of methods have been developed over time to extract knowledge from such resources (Maleki et al., 2020). The very first methods used a simple approach to identify the gene sets that are enriched in differentially expressed genes (Khatri et al., 2002;Draghici et al., 2003b;Dennis et al., 2003). This approach has various limitations including the fact that it ignores the magnitude of the measured gene expressions. This was addressed by the second generation of methods, pioneered by GSEA (Subramanian et al., 2005), and called functional class scoring (FCS). FCS methods use the correlation between gene expression and the phenotype but still ignore all the interactions between genes. This was addressed by the third generation of methods, called topology-based, or pathway analysis methods. The first such method, impact analysis (Draghici et al., 2007;Tarca et al., 2009), was soon followed by a plethora of over 20 other approaches (Khatri et al., 2012;Mitrea et al., 2013;Nguyen et al., 2018). Many of these methods have been bench-marked recently (Nguyen et al., 2019).Even though pathway analysis methods are very different from enrichment and FCS methods, we will use "gene set analysis" to generically refer to the entire family of methods aimed at understanding the
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.013 | 0.016 |
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".