GeneTerpret: a customizable multilayer approach to genomic variant prioritization and interpretation
Bibliographic record
Abstract
ABSTRACT Variant interpretation is the main bottleneck in medical genomic sequencing efforts. This usually involves genome analysts manually scouring through a multitude of independent databases, often with the aid of several and mostly independent computational tools. To streamline the variant interpretation process, we developed GeneTerpret platform that collates data from current interpretation tools and databases, and applies a phenotype-driven query to categorize the variants identified in a given genome. The platform assigns quantitative validity scores to genes by query and assembly of the current genotype-phenotype data, sequence homology, molecular interactions, expression data, and animal models. The platform uses the American College of Medical Genetics (ACMG) criteria to categorize variants into five tiers (from benign to pathogenic). The platform then outputs a prioritized list of potentially causal variants/genes in a given genome for a specific case. GeneTerpret is a flexible and free platform designed to streamline the variant interpretation process through a unique interface, with improved ease, speed and accuracy. This unique integrated system provides effective validity and pathogenicity modules to assess genetic variant data and allows the user to decide which output and impact level should be considered in this process. The platform can be accessed and used online at https://geneterpret.com .
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".