Bibliographic record
Abstract
Personalized medicine requires rapid and accurate classification of pathogenic human variation. With over 50% of clinically interpreted missense variants classified as “variants of uncertain significance” (VUSes), multiple approaches are needed to improve variant interpretation. Multiplexed assays of variant effect (MAVEs) can experimentally test nearly all possible missense variants in selected protein targets, while computational methods seek to infer missense variant impacts using statistical modelling. Here I describe work to assist and exploit both MAVE and computational studies.To assist in planning and to promote collaboration and efficient communication for MAVE studies, I developed: 1) strategies to prioritize genes likely to have a greater impact on clinical variant interpretation, 2) MaveQuest, a resource to help researchers identify MAVE target genes and explore potential assays, and 3) MaveRegistry, a community resource for sharing MAVE progress and finding collaborators. To compare the performance of variant effect predictors and to exploit both experimental and computational information about variant impact, I 1) assessed computational variant effect predictors using a large prospective cohort, and 2) developed a pipeline to screen human pseudogenes for which different genetic variant interpretation might re-classify these pseudogenes to be protein-coding genes, refining human genome annotation.
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.
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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".