Author

John H Tay

The University of MelbourneORCID 0009-0002-0721-4354Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:Bayesian inference· 2 Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Research Focus:gene regulatory network inference· 1 Research Focus:phylogenetics / evolution· 1 Assay:protein sequence· 1 Approach:statistical model· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Sebastián DuchêneiD· 2 shared Arthur KocheriD· 1 shared Guy BaeleiD· 1 shared

Activity by Year

2023-2024 · peak 1/yr

Co-Author Collaboration Network

3 top collaborators · 2 mutual co-author links

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2 shared papersBioinformatics

Sebastián DuchêneORCID

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2 papers

Recent·Most cited

BioinformaticsPLoS Computational BiologyPeer-reviewedNov 6, 2024

Assessing the effect of model specification and prior sensitivity on Bayesian tests of temporal signal

John H Tay, Arthur Kocher, Sebastián Duchêne

The paper evaluates how model and prior choices affect the Bayesian temporal signal detection method BETS and provides guidelines for reliable molecular clock estimates.

5 citations

bacteria (other)protein sequence

𝕏 PostBluesky

BioinformaticsMolecular Biology and EvolutionPeer-reviewedSep 21, 2023

Detecting episodic evolution through Bayesian inference of molecular clock models

John H Tay, Guy Baele, Sebastián Duchêne

This paper introduces a Bayesian method to detect episodic evolution by comparing molecular clock models and applies it to SARS-CoV-2 data, finding strong evidence for episodic evolution.

7 citations

viruswhole-genome sequencing

𝕏 PostBluesky