Gene4Denovo2: an updated platform for human <i>de novo</i> mutations discovery and interpretation
Bibliographic record
Abstract
De novo mutations (DNMs) drive evolution and increase biodiversity, yet concurrently act as a cryptic cause of numerous genetic diseases. Here, we present Gene4Denovo2 (https://genemed.tech/gene4denovo2/), an updated version of the Gene4Denovo, aiming to provide a more comprehensive DNM datasets and their interpretations. The key improvements include the following: (i) We have substantially expanded the number and scope of DNMs, associated samples, and phenotypes. Specifically, Gene4Denovo2 now contains 1 626 050 DNMs from 130 439 individuals across 96 phenotypes. Moreover, clinical information covering nearly 1000 items has been added for 448 096 individuals, including those with DNMs and their unaffected family members. (ii) We have introduced new features to assist in the evaluation of DNMs, including support for ACMG rating and the addition of gene prioritization scores for rapid classification and filtering of candidate genes. (iii) An upgraded analysis interface allowing flexible annotation configuration and a significant expansion in the number of annotatable datasets. Additionally, a new integrated tool enables DNMs calling from family-based sequencing data. In summary, Gene4Denovo2 provides a more extensive collection of DNMs, enhanced annotation capabilities, and upgraded analysis tools, which will facilitate a deeper exploration of the role of DNMs in disease pathogenesis.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.021 |
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".