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Oliver M Crook

Showing results (1-10 of 29) with videos related to

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Statistical Applications in Genetics and Molecular Biology|December 13, 2019
Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomicsOliver M Crook, Laurent Gatto, Paul D W Kirk
Communications Biology|June 15, 2022
Empirical Bayes functional models for hydrogen deuterium exchange mass spectrometryOliver M Crook, Chun-Wa Chung, Charlotte M Deane
Journal of Proteome Research|March 8, 2022
Challenges and Opportunities for Bayesian Statistics in ProteomicsOliver M Crook, Chun-Wa Chung, Charlotte M Deane
Proteomics|June 20, 2020
Moving Profiling Spatial Proteomics Beyond Discrete ClassificationOliver M Crook, Tom Smith, Mohamed Elzek, et al.
Communications Chemistry|November 10, 2025
Inferring residue level hydrogen deuterium exchange with ReXOliver M Crook, Nathan Gittens, Chun-Wa Chung, et al.
Journal of Proteome Research|August 15, 2023
A Functional Bayesian Model for Hydrogen-Deuterium Exchange Mass SpectrometryOliver M Crook, Nathan Gittens, Chun-Wa Chung, et al.
The Annals of Applied Statistics|December 12, 2022
Semi-Supervised Non-Parametric Bayesian Modelling of Spatial ProteomicsOliver M Crook, Kathryn S Lilley, Laurent Gatto, et al.
Nature Computational Science|January 4, 2024
Improving lab-of-origin prediction of genetically engineered plasmids via deep metric learningIgor M Soares, Fernando H F Camargo, Adriano Marques, et al.
Communications Biology|June 30, 2021
A Bayesian semi-parametric model for thermal proteome profilingSiqi Fang, Paul D W Kirk, Marcus Bantscheff, et al.
Plos Computational Biology|November 28, 2018
A Bayesian mixture modelling approach for spatial proteomicsOliver M Crook, Claire M Mulvey, Paul D W Kirk, et al.
Pageof 3

Showing results (1-10 of 29) with videos related to

Sort By:
Pageof 3
Statistical Applications in Genetics and Molecular Biology|December 13, 2019
Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomicsOliver M Crook, Laurent Gatto, Paul D W Kirk
Communications Biology|June 15, 2022
Empirical Bayes functional models for hydrogen deuterium exchange mass spectrometryOliver M Crook, Chun-Wa Chung, Charlotte M Deane
Journal of Proteome Research|March 8, 2022
Challenges and Opportunities for Bayesian Statistics in ProteomicsOliver M Crook, Chun-Wa Chung, Charlotte M Deane
Proteomics|June 20, 2020
Moving Profiling Spatial Proteomics Beyond Discrete ClassificationOliver M Crook, Tom Smith, Mohamed Elzek, et al.
Communications Chemistry|November 10, 2025
Inferring residue level hydrogen deuterium exchange with ReXOliver M Crook, Nathan Gittens, Chun-Wa Chung, et al.
Journal of Proteome Research|August 15, 2023
A Functional Bayesian Model for Hydrogen-Deuterium Exchange Mass SpectrometryOliver M Crook, Nathan Gittens, Chun-Wa Chung, et al.
The Annals of Applied Statistics|December 12, 2022
Semi-Supervised Non-Parametric Bayesian Modelling of Spatial ProteomicsOliver M Crook, Kathryn S Lilley, Laurent Gatto, et al.
Nature Computational Science|January 4, 2024
Improving lab-of-origin prediction of genetically engineered plasmids via deep metric learningIgor M Soares, Fernando H F Camargo, Adriano Marques, et al.
Communications Biology|June 30, 2021
A Bayesian semi-parametric model for thermal proteome profilingSiqi Fang, Paul D W Kirk, Marcus Bantscheff, et al.
Plos Computational Biology|November 28, 2018
A Bayesian mixture modelling approach for spatial proteomicsOliver M Crook, Claire M Mulvey, Paul D W Kirk, et al.
Pageof 3