Bibliographic record
Abstract
The underlying disease mechanisms likely include mutation effects on the nuclear envelope and on interactions between lamins and transcription factors. At the same time, can a simple genomic attribute -- for instance, mutation position within the LMNA sequence -- predict the complex phenotypic effects? In order to assess this, hierarchical cluster analysis (HCA) was used for assembling 16 laminopathies into two classes based on organ system involvement. Ninety-one reported causative LMNA mutations in these laminopathies were then classified according to their position upstream or downstream of the nuclear localization signal sequence (NLS). Contingency analysis was used in order to assess a non-random relationship between HCA laminopathy class and LMNA mutation position relative to the NLS. HCA laminopathy class and LMNA mutation position were strongly associated (p < 0.0001). The odds ratio for general association between an HCA class 1 laminopathy and a mutation upstream of the NLS sequence was 8.4 (95% confidence interval = 2.9 - 24.7, p < 0.0001). Although the underlying molecular biology is complex, the findings support the hypothesis that laminopathy phenotype and LMNA genotype are non-randomly associated. Furthermore, HCA may be a tool to help with the study of phenotype - genotype associations, or 'phenomics'.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".