A004 A markov chain-based bayesian population model for somatic instability in human white blood cells
Bibliographic record
Abstract
Using a database of HTT CAG-length distributions determined by amplicon sequencing in White Blood Cells (WBCs) collected from 1447 participant-visits from the Registry and Enroll-HD Studies, we build a Bayesian population model based on the assumption that CAG length distributions evolve over time according to a continuous time Markov chain in which changes in CAG length occur in single CAG length jumps. These Markov chains have jump rates that increase with CAG length, resulting in both expansions and contractions with a bias toward expansion. The effects of PCR-induced slippage were corrected using single molecule data, a novel multinomial logit model and a novel algorithm based on convex programming. Models were fit to the PCR slippage-corrected data using a fast BFGS algorithm in R. This algorithm minimizes both least squares and Kullback-Liebler loss functions and uses analytic gradients of the matrix exponential function. Participant-level models are shown to fit very well and predict CAG length distributions at the second visit 2 from distributions at visit 1. Model parameters for jump rate and expansion bias are correlated with inherited CAG length, CAP score, age-at-baseline, and time- between-visits. Finally, a Bayesian population-based model (implemented in the Stan Statistical Software Package) was used to model the precision of the participant-level model fits using a novel application of the Dirichlet distribution. Implications for studies of somatic instability in other cell types are discussed as are the uses of the statistical methodology described here in biomarker development.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".