A Bayesian mixture modelling approach for spatial proteomics. - Oliver M. Crook; Claire M. Mulvey; Paul D. W. Kirk; Kathryn S. Lilley; Laurent Gatto (PLoS Comput. Biol., 2018)

PDE-Inspired Algorithms for Semi-Supervised Learning on Point Clouds. - Oliver M. Crook; Tim Hurst; Carola-Bibiane Schönlieb; Matthew Thorpe; Konstantinos C. Zygalakis (CoRR, 2019)

A Bioconductor workflow for the Bayesian analysis of spatial proteomics. - Oliver M. Crook; Lisa M. Breckels; Kathryn S. Lilley; Paul D. W. Kirk; Laurent Gatto (F1000Research, 2019)

A Linear Transportation Lp Distance for Pattern Recognition. - Oliver M. Crook; Mihai Cucuringu; Tim Hurst; Carola-Bibiane Schönlieb; Matthew Thorpe; Konstantinos C. Zygalakis (CoRR, 2020)

A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection. - Oliver M. Crook; Aikaterini Geladaki; Daniel J. H. Nightingale; Owen L. Vennard; Kathryn S. Lilley; Laurent Gatto; Paul D. W. Kirk (PLoS Comput. Biol., 2020)

Analysis of the first Genetic Engineering Attribution Challenge. - Oliver M. Crook; Kelsey Lane Warmbrod; Greg Lipstein; Christine Chung 002; Christopher W. Bakerlee; T. Greg McKelvey Jr.; Shelly R. Holland; Jacob L. Swett; Kevin M. Esvelt; Ethan C. Alley; William J. Bradshaw (CoRR, 2021)

Deep metric learning improves lab of origin prediction of genetically engineered plasmids. - Igor Muniz Soares; Fernando H. F. Camargo; Adriano Marques; Oliver M. Crook (CoRR, 2021)

Improving lab-of-origin prediction of genetically engineered plasmids via deep metric learning. - Igor Muniz Soares; Fernando H. F. Camargo; Adriano Marques; Oliver M. Crook (Nat. Comput. Sci., 2022)

A linear transportation Lp distance for pattern recognition. - Oliver M. Crook; Mihai Cucuringu; Tim Hurst; Carola-Bibiane Schönlieb; Matthew Thorpe; Konstantinos C. Zygalakis (Pattern Recognit., 2024)