An integrative scoring system for ranking SNPs by their potential deleterious effects
Bibliographic record
Abstract
MOTIVATION: Identifying single nucleotide polymorphisms (SNPs) that underlie common and complex human diseases, such as cancer, is of major interest in current molecular epidemiology. Nevertheless, the tremendous number of SNPs on the human genome requires computational methods for prioritizing SNPs according to their potentially deleterious effects to human health, and as such, for expediting genotyping and analysis. As of yet, little has been done to quantitatively assess the possible deleterious effects of SNPs for effective association studies. RESULTS: We propose a new integrative scoring system for prioritizing SNPs based on their possible deleterious effects within a probabilistic framework. We applied our system to 580 disease-susceptibility genes obtained from the OMIM (Online Mendelian Inheritance in Man) database, which is one of the most widely used databases of human genes and genetic disorders. The scoring results clearly show that the distribution of the functional significance (FS) scores for already known disease-related SNPs is significantly different from that of neutral SNPs. In addition, we summarize distinct features of potentially deleterious SNPs based on their FS score, such as functional genomic regions where they occur or bio-molecular functions that they mainly affect. We also demonstrate, through a comparative study, that our system improves upon other function-assessment systems for SNPs, by assigning significantly higher FS scores to already known disease-related SNPs than to neutral SNPs.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".