PSEA: A phenotypic similarity ensemble approach for prioritizes candidate genes to aid mendelian disease diagnosis
Bibliographic record
Abstract
Abstract Motivation Next-generation sequencing (NGS) is increasingly applied to the molecular diagnosis of genetic disorders. However, challenges for the interpretation of NGS data remain given the massive number of variants produced by NGS. Careful assessment is required to identify the most likely disease-causing variants that best match the patients’ clinical phenotypes, which is highly experience-dependent and of low cost-effectiveness. Results The human phenotype ontology (HPO) together with the information content (IC) are widely used for phenotypic similarity evaluation. Here, we introduce PSEA, a new phenotypic similarity evaluation tool capable of quantifying groups of HPO terms unbiasedly. By comparing with other methods, PSEA show optimal performance and show a higher tolerance to phenotypic noise or incompleteness. We also developed a web server for disease-causing gene prioritization and HPO-gene weighted linkage visualization. Availability Source code and Web service are free available at https://github.com/zhonghua-wang/psea and https://phoenix.bgi.com/psea , respectively. Contact wangzhonghua@genomics.cn Supplementary information Supplementary data are available at Bioinformatics online.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".