Workshop report: the clinical application of data from multiplex assays of variant effect (MAVEs), 12 July 2023
Bibliographic record
Abstract
Clinical classification of genomic variants identified on sequencing is often challenging, with many variants classified as Variants of Uncertain Significance (VUS) on account of insufficient evidence. Advances in sequencing and gene synthesis has made feasible multiplexed assays of variant effect (MAVEs), which quantify the functional impact of many thousands of genomic variants in a single experiment. These assays and the functional evidence they generate have the potential to empower more accurate clinical variant classification. However, there are many outstanding challenges and opportunities that require joint resolution and specification, thus necessitating communication between the research scientists who have designed and performed MAVEs and the clinicians and diagnostic scientists who will apply their data to clinical variant classification. In the ‘Clinical Application of MAVE Data’ workshop, held on 12th July 2023 at the Wellcome Connecting Science Conference Centre in between two relevant research meetings, ‘Curating the Clinical Genome 2023’ and the ‘Mutational Scanning Symposium 2023’, 44 key scientific and/or clinical stakeholders were brought together to consider important questions relating to clinical application of MAVE data, such as quantitative validation, variant truth-sets, platforms and standards for dissemination of MAVE data. The outcomes and possible next steps that were discussed encompassed development of focused workshops to develop consensus recommendations, creating a MAVE evaluation working group, and collaboration of ClinVar and MaveDB to enact software changes that support enhanced functional data submission.
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 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".