The proportion of mutations predicted to have a deleterious effect differs between gain and loss of function genes in neurodegenerative disease
Bibliographic record
Abstract
As more studies are turning to bioinformatic prediction programs to assess the potential impact of amino acid substitutions, it is relevant to evaluate the prediction results these programs give in genes that have been well-characterized for Mendelian diseases. Eight genes responsible for neurodegenerative disease with many identified mutations were sub-grouped into those that either have a gain or loss of function disease mechanism. Three prediction programs, PolyPhen, Panther and SIFT, were queried for the reported missense mutations. The mean percent of benign mutations was significantly higher in gain of function genes using the PolyPhen program (38% versus 21%, p=0.007). The probability that a gain of function mutation was predicted to have a damaging role was also significantly less using the Panther program (p=4.86x10(-12)). In contrast, there was no difference between gain and loss of function gene when the SIFT program was used. However, the most accurate distinction between gain and loss of function genes could be obtained when considering the mutations for which all three programs predicted the same result. Further, stratification of SOD1 mutations indicated that only the PolyPhen program could distinguish mutations that impaired enzymatic activity of SOD1 from those with near wildtype activity. The profile of benign and damaging changes from these genes will aid in the interpretation of bioinformatic prediction program results from missense mutations identified in novel genes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".