Linking genes to diseases: it's all in the data
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
Genome-wide association analyses on large patient cohorts are generating large sets of candidate disease genes. This is coupled with the availability of ever-increasing genomic databases and a rapidly expanding repository of biomedical literature. Computational approaches to disease-gene association attempt to harness these data sources to identify the most likely disease gene candidates for further empirical analysis by translational researchers, resulting in efficient identification of genes of diagnostic, prognostic and therapeutic value. Existing computational methods analyze gene structure and sequence, functional annotation of candidate genes, characteristics of known disease genes, gene regulatory networks, protein-protein interactions, data from animal models and disease phenotype. To date, a few studies have successfully applied computational analysis of clinical phenotype data for specific diseases and shown genetic associations. In the near future, computational strategies will be facilitated by improved integration of clinical and computational research, and by increased availability of clinical phenotype data in a format accessible to computational approaches.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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