Horizon: CNV interpretation through rapid automated ACMG-aligned pathogenicity analysis
Bibliographic record
Abstract
Abstract Purpose Our study assesses the Horizon model, a novel CNV classification tool developed in line with American College of Medical Genetics (ACMG) guidelines, to enhance the classification of pathogenicity in CNVs. Methods Horizon utilizes a ranking-based algorithm, incorporating multiple proprietary databases and variant inheritance models as per ACMG standards. The model’s effectiveness was verified through Area Under the Curve (AUC) analyses on three datasets comprising 696 pathogenic inherited or de novo variants, as classified by clinical geneticists and several established tools. Results Horizon achieved an AUC of 0.97 in the discovery cohort, demonstrating high accuracy in CNV interpretation and proficiency in predicting pathogenicity. We observed an AUC of 0.87 in the de novo variant cohort and an overall AUC of 0.94 across all cohorts, surpassing tools like ClassifyCNV and AnnotSV. It showed particular effectiveness in interpreting duplication CNVs and the highest performance for CNVs sized 3-5 Mb. Conclusion The Horizon model offers robust and accurate CNV interpretation, outperforming existing tools and aligning closely with clinical evaluations. Its comprehensive approach, integrating a range of genomic features and following ACMG guidelines, makes it a crucial tool in the genomic interpretation landscape, facilitating the rapid and accurate diagnosis of genetic disorders.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".