Seven years since the launch of the Matchmaker Exchange: the evolution of genomic matchmaking
Bibliographic record
Abstract
The Matchmaker Exchange (MME) launched in 2015 to provide a robust mechanism to discover novel disease-gene relationships. This federated network connects databases holding relevant data, where two or more users are looking for a match for the same gene (two-sided matchmaking). The number of unique genes present across MME has steadily increased; there are currently >13,520 unique genes (~68% of all protein coding genes) connected across MME’s nodes, GeneMatcher, DECIPHER, PhenomeCentral, MyGene2, seqr, Initiative on Rare and Undiagnosed Disease, PatientMatcher, and the RD-Connect Genome-Phenome Analysis Platform. The dataset accessible across MME includes more than 120,000 cases from over 12,000 contributors in 98 countries. Discovery of potential new disease-gene relationships occurs daily and international collaborations are moving these connections forward to publication. Expansion of data sharing into routine clinical practice has ensured access to discovery for even more individuals with undiagnosed rare genetic disease. MME supports connections to the literature (PubCaseFinder) and to human and model organism resources (Monarch Initiative) and scientists (ModelMatcher). Efforts are underway to explore additional approaches to matchmaking where there is only one querier (one-sided matchmaking). Genomic matchmaking has proven its utility over the past 7 years and will continue to facilitate discoveries in years to come.
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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 | 0.067 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".