Looking Across Protein Domains to Identify Driver Mutations in Cancer
Bibliographic record
Abstract
Abstract Cancer can develop through the accumulation of somatic mutations that drive uncontrolled cell proliferation. A central objective in cancer research is to identify mutations that provide a selective growth advantage to tumor cells, so called driver mutations. Many computational methods infer driver missense mutations in proteins by assessing their recurrence. However, such approach suffers from the limited capacity to detect those driver mutations that occur infrequently across tumor samples. One strategy to overcome this limitation is to aggregate mutations from proteins sharing the same protein domain. Here we constructed a benchmark of cancer driver and passenger mutations, based on the known experimental and clinical studies, and systematically evaluated the applicability of methods that aggregate mutations across different mutation types and protein domains. We found that accounting for evidence mutations from different types of amino acid substitutions occurring in the same protein position enhances the classification performance. Furthermore, accounting for evidence mutations from paralogous proteins in the domain family increased the precision but compromised the overall classification accuracy. In addition, the performance of domain-based approaches was shown to crucially depend on the similarity between the target and evidence proteins.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".