Paul D. W. Kirk

According to our database, Paul D. W. Kirk authored at least 20 papers between 2009 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Dose-response prediction for in-vitro drug combination datasets: a probabilistic approach.

Leiv Rønneberg, Paul D. W. Kirk, Manuela Zucknick

BMC Bioinform., December, 2023

2022

Consensus clustering for Bayesian mixture models.

Stephen Coleman, Paul D. W. Kirk, Chris Wallace

BMC Bioinform., 2022

2021

Detection of quantitative trait loci from RNA-seq data with or without genotypes using BaseQTL.

Elena Vigorito, Wei-Yu Lin, Colin Starr, Paul D. W. Kirk, Simon R. White, Chris Wallace

Nat. Comput. Sci., 2021

MR-Clust: clustering of genetic variants in Mendelian randomization with similar causal estimates.

Christopher N. Foley, Amy M. Mason, Paul D. W. Kirk, Stephen Burgess

Bioinform., 2021

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., November, 2020

GPseudoClust: deconvolution of shared pseudo-profiles at single-cell resolution.

Magdalena E. Strauß, Paul D. W. Kirk, John E. Reid, Lorenz Wernisch

Bioinform., 2020

Multiple kernel learning for integrative consensus clustering of omic datasets.

Alessandra Cabassi, Paul D. W. Kirk

Bioinform., 2020

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

Multiple kernel learning for integrative consensus clustering of genomic datasets.

Alessandra Cabassi, Paul D. W. Kirk

CoRR, 2019

2018

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

2016

Inferring extrinsic noise from single-cell gene expression data using approximate Bayesian computation.

Oleg Lenive, Paul D. W. Kirk, Michael P. H. Stumpf

BMC Syst. Biol., 2016

MEANS: python package for Moment Expansion Approximation, iNference and Simulation.

Sisi Fan, Quentin Geissmann, Eszter Lakatos, Saulius Lukauskas, Angelique Ale, Ann C. Babtie, Paul D. W. Kirk, Michael P. H. Stumpf

Bioinform., 2016

2015

SYSBIONS: nested sampling for systems biology.

Rob Johnson, Paul D. W. Kirk, Michael P. H. Stumpf

Bioinform., 2015

2014

Model Selection in Systems Biology Depends on Experimental Design.

Daniel Silk, Paul D. W. Kirk, Chris P. Barnes, Tina Toni, Michael P. H. Stumpf

PLoS Comput. Biol., 2014

Derivative processes for modelling metabolic fluxes.

Justina Zurauskiene, Paul D. W. Kirk, Thomas Thorne, John Pinney, Michael P. H. Stumpf

Bioinform., 2014

2013

Balancing the Robustness and Predictive Performance of Biomarkers.

Paul D. W. Kirk, Aviva Witkover, Charles R. M. Bangham, Sylvia Richardson, Alexandra M. Lewin, Michael P. H. Stumpf

J. Comput. Biol., 2013

2012

Bayesian correlated clustering to integrate multiple datasets.

Paul D. W. Kirk, Jim E. Griffin, Richard S. Savage, Zoubin Ghahramani, David L. Wild

Bioinform., 2012

2011

Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements.

Emma J. Cooke, Richard S. Savage, Paul D. W. Kirk, Robert Darkins, David L. Wild

BMC Bioinform., 2011

2010

ABC-SysBio - approximate Bayesian computation in Python with GPU support.

Juliane Liepe, Chris P. Barnes, Erika Cule, Kamil Erguler, Paul D. W. Kirk, Tina Toni, Michael P. H. Stumpf

Bioinform., 2010

2009

Gaussian process regression bootstrapping: exploring the effects of uncertainty in time course data.

Paul D. W. Kirk, Michael P. H. Stumpf

Bioinform., 2009