DVA: predicting the functional impact of single nucleotide missense variants
Bibliographic record
Abstract
BACKGROUND: In the past decade, single nucleotide variants (SNVs) have been identified as having a significant relationship with the development and treatment of diseases. Among them, prioritizing missense variants for further functional impact investigation is an essential challenge in the study of common disease and cancer. Although several computational methods have been developed to predict the functional impacts of variants, the predictive ability of these methods is still insufficient in the Mendelian and cancer missense variants. RESULTS: We present a novel prediction method called the disease-related variant annotation (DVA) method that predicts the effect of missense variants based on a comprehensive feature set of variants, notably, the allele frequency and protein-protein interaction network feature based on graph embedding. Benchmarked against datasets of single nucleotide missense variants, the DVA method outperforms the state-of-the-art methods by up to 0.473 in the area under receiver operating characteristic curve. The results demonstrate that the proposed method can accurately predict the functional impact of single nucleotide missense variants and substantially outperforms existing methods. CONCLUSIONS: DVA is an effective framework for identifying the functional impact of disease missense variants based on a comprehensive feature set. Based on different datasets, DVA shows its generalization ability and robustness, and it also provides innovative ideas for the study of the functional mechanism and impact of SNVs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".