Domain-restricted mutation analysis to identify novel driver events in human cancer
Bibliographic record
Abstract
Analysis of mutational spectra across various cancer types has given valuable insights into tumorigenesis. Different approaches have been used to identify novel drivers from the set of somatic mutations, including the methods which use sequence conservation, geometric localization and pathway information. Recent computational methods suggest use of protein domain information for analysis and understanding of the functional consequence of non-synonymous mutations. Similarly, evidence suggests recurrence at specific position in proteins is robust indicators of its functional impact. Building on this, we performed a systematic analysis of TCGA exome derived somatic mutations across 6089 PFAM domains and significantly mutated domains were identified using randomization approach. Multiple alignment of individual domain allowed us to prioritize for conserved residues mutated at analogous positions across different proteins in a statistically disciplined manner. In addition to the known frequently mutated genes, this analysis independently identifies low frequency Meprin and TRAF-Homology (MATH) domain in Speckle Type BTB/POZ (SPOP) protein, in prostate adenocarcinoma. Results from this analysis will help generate hypotheses about the downstream molecular mechanism resulting in cancer phenotypes.
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 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 | 0.001 | 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".