Inferring the function of genes based on recurrent mutations in protein domains: Analysis of OncoMD data
Bibliographic record
Abstract
Protein domains are conserved structural and functional units of proteins. We have used OncoMD data to analyse recurrent mutations in protein domains and their functional impact. In this study, we systematically analysed tumour samples from 28 different cancer groups to identify recurrent mutations, their positions within specific domains to identify domains that harbour recurrent mutations in different cancers. Next, we mapped the mutations to protein domains present in oncogenes and tumour suppressor genes and identified a variety of domains that are enriched for mutations. Whereas kinase domain and p53 superfamily domains are significantly mutated across many cancers, few cancers, such as melanoma, brain and colorectal cancers are significantly mutated in 7-transmembrane domain and Ig superfamily domain proteins. Highly mutated protein domains such as the PKc and PI3K superfamily are targets of anti-cancer drugs. Inferring the functional impact of recurrent mutations in cancer is an important objective of cancer genomics. Analysis of mutation hotspots in protein domains and its functional impact will provide novel insights into disease mechanisms and breakthrough therapeutics for treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".