Current trends in publicly available genetic databases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it