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Accelerating Bayesian inference of dependency between mixed-type biological traits — Europe PMC PMID:37639445

Source: Europe PMC (en)
Original: https://europepmc.org/article/MED/37639445
DOI: 10.1371/journal.pcbi.1011419
License (as archived): CC0-1.0
Archived: 2026-10-07T12:27:12.098Z
SHA-256 of the archived text: b8a973f718ec15821eddc60f7e7d14bb063e76771d589ce9f436d4e05fffd813
Stable URL of this page: https://metraipolis.com/documento/dc_ada54955ca30

How to cite

Accelerating Bayesian inference of dependency between mixed-type biological traits — Europe PMC PMID:37639445 — Europe PMC (en), https://europepmc.org/article/MED/37639445 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ada54955ca30 (Metraipolis archive dc_ada54955ca30, sha256 b8a973f718ec15821eddc60f7e7d14bb063e76771d589ce9f436d4e05fffd813)

BibTeX

@misc{dcada54955ca30,
  title = {Accelerating Bayesian inference of dependency between mixed-type biological traits — Europe PMC PMID:37639445},
  howpublished = {Europe PMC (en)},
  url = {https://europepmc.org/article/MED/37639445},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ada54955ca30; sha256 b8a973f718ec15821eddc60f7e7d14bb063e76771d589ce9f436d4e05fffd813},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: Europe PMC (en), https://europepmc.org/article/MED/37639445. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Inferring dependencies between mixed-type biological traits while accounting for evolutionary relationships between specimens is of great scientific interest yet remains infeasible when trait and specimen counts grow large. The state-of-the-art approach uses a phylogenetic multivariate probit model to accommodate binary and continuous traits via a latent variable framework, and utilizes an efficient bouncy particle sampler (BPS) to tackle the computational bottleneck-integrating many latent variables from a high-dimensional truncated normal distribution. This approach breaks down as the number of specimens grows and fails to reliably characterize conditional dependencies between traits. Here, we propose an inference pipeline for phylogenetic probit models that greatly outperforms BPS. The novelty lies in 1) a combination of the recent Zigzag Hamiltonian Monte Carlo (Zigzag-HMC) with linear-time gradient evaluations and 2) a joint sampling scheme for highly correlated latent variables and correlation matrix elements. In an application exploring HIV-1 evolution from 535 viruses, the inference requires joint sampling from an 11,235-dimensional truncated normal and a 24-dimensional covariance matrix. Our method yields a 5-fold speedup compared to BPS and makes it possible to learn partial correlations between candidate viral mutations and virulence. Computational speedup now enables us to tackle even larger problems: we study the evolution of influenza H1N1 glycosylations on around 900 viruses. For broader applicability, we extend the phylogenetic probit model to incorporate categorical traits, and demonstrate its use to study Aquilegia flower and pollinator co-evolution.

Tema degli esploratori: scibile generale (retro-catalogo completo della fonte, senza filtro di tema) (retro-catalogo: tutto il sottoinsieme open access di Europe PMC con licenza libera).
Pubblicazione: PLoS computational biology, 2023 (prima pubblicazione 2023-08-28).
Identificativi: PMID 37639445 · PMCID PMC10491301 · DOI 10.1371/journal.pcbi.1011419.
Descrittori MeSH principali: Influenza A Virus, H1N1 Subtype.
Pagina: https://europepmc.org/article/MED/37639445 — https://doi.org/10.1371/journal.pcbi.1011419. Autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza dell'articolo dichiarata da Europe PMC: CC0 (la versione è indicata dall'editore sulla pagina). Abstract riportato senza modifiche al contenuto, con attribuzione; deposito marcato CC0.
Letto il 2026-10-07T11:59:26.266Z tramite l'API REST ufficiale di Europe PMC (EMBL-EBI), nel rispetto del robots.txt e del crawl-delay.