Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus
Bibliographic record
Abstract
Vaccines created from closely related viruses are vital for offering protection against newly emerging strains. For Foot-and-Mouth dis-ease virus (FMDV), where multiple serotypes co-circulate, testing large numbers of vac-cines can be infeasible. Therefore the de-velopment of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Here we de-scribe a novel sparse Bayesian variable selec-tion model using spike and slab priors which is able to predict antigenic variability and identify sites which are important for the neu-tralisation of the virus. We are able to iden-tify multiple residues which are known to be key indicators of antigenic variability. Many of these were not identified previously us-ing Frequentist mixed-effects models and still cannot be found when an `1 penalty is used. We further explore how the Markov chain Monte Carlo (MCMC) proposal method for the inclusion of variables can offer signif-icant reductions in computational require-ments, both for spike and slab priors in gen-eral, and our hierarchical Bayesian model in particular. 1
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 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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".