Mapping the PTEN Mutation Landscape: Structural and Functional Drivers of Lung Cancer
Bibliographic record
Abstract
Abstract Lung cancer is the predominant form of cancer globally, arising from the dysfunction of genetic mutations. Although PTEN mutation is crucial in the aetiology of lung cancer, the mapping of these major drivers has to be determined. We leverage computational algorithms on 43,855 SNPs of PTEN to discover the mutational impact contributing to lung cancer. Fifteen variations were identified as detrimental, and no pertinent studies have previously addressed their structural and functional aspects. Notably, seven variations were identified as the most significant contributors to lethal effects in functional aberration, as demonstrated by the computational assessment. Subsequently, molecular simulation elucidated the structural instability associated with these alterations. Furthermore, drug binding experiments at the mutational site corroborated the destabilization experiments by demonstrating the conformational alteration of the structure, resulting in varied amino acid interactions. In summary, the present study elucidates the influence of mutations in PTEN structure on its functional architecture.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".