PhenomeCentral: An Integrated Portal for Sharing and Searching Patient Phenotype Data for Rare Genetic Disorders.
Bibliographic record
Abstract
The availability of low-cost genome sequencing has allowed for the identification of the molecular cause of hundreds of rare genetic disorders. Solved disorders, however, only represent the “tip of the iceberg”. Because the discovery of disease-causing variants typically requires confirmation of the mutation or gene in multiple unrelated individuals, an even larger number of genetic disorders remain unsolved due to difficulty identifying second families. With many groups now tackling these remaining undiagnosed disorders, which may be present in only a handful of individuals seen at different hospitals and sequenced by different centers, it is critical to establish effective and secure data-sharing techniques that allow clinicians and scientists to identify additional families via phenotype and genotype searches. To address this need, we have developed PhenomeCentral (http://phenomecentral.org), a repository for secure data sharing targeted to the rare disorder community. Each patient record within PhenomeCentral consists of a thorough phenotypic description capturing observed abnormalities as well as relevant absent manifestations, expressed using Human Phenotype Ontology terms. Furthermore, each record can be labeled by the creator as: private ‒ hidden from everyone except the contributor; public ‒ viewable and searchable by all registered users; or matchable ‒ the record cannot be directly viewed or searched, but is reachable via an automated phenotype matching system (following Cafe Variome principles) which informs contributors of the existence of profiles similar to their cases. PhenomeCentral currently incorporates phenotype data for hundreds of patients with rare genetic disorders without a molecular diagnosis, including ongoing submissions from the Canadian CARE for RARE project and the NIH Undiagnosed Diseases Program (UDP). Clinical geneticists and scientists studying rare disorders can request accounts, and new patients can be added either using the PhenoTips User Interface, built into PhenomeCentral, or uploaded in bulk.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.030 |
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".