Current trends in publicly available genetic databases
Bibliographic record
Abstract
Analysis of human genetic data promises to uncover important disease targets. Genes known to cause or increase susceptibility for various diseases are being identified through analysis of genetic data, expression and metabolites. Future benefits to individuals are far-reaching, including improved gene therapy strategies, better drug development for disease treatment, pre-symptomatic disease intervention and risk susceptibility information. The rapid expansion of genetic databases has resulted in the emerging areas of genomics, transcriptomics, proteomics and metabolomics. The article presents a comprehensive overview of Internet databases, their trends over time and what 'omics' type they embody. With the completion of the human genome we are entering the postgenomic era. The use of microarrays and database software for genomic, transcriptomic, proteomic and metabolomic data for clinical assays and new diagnostic therapeutics will result in large, interlinked databases that will present unique issues of data management, standardization and information sharing.
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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 | 0.024 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.019 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".