Michael P. H. Stumpf

发表

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

Paul D. W. Kirk, Michael P. H. Stumpf, M. Stumpf, 2009, Bioinform..

Multi-model inference of network properties from incomplete data

Michael P. H. Stumpf, Thomas Thorne, M. Stumpf, 2006, J. Integr. Bioinform..

Trees on networks: resolving statistical patterns of phylogenetic similarities among interacting proteins

Michael P. H. Stumpf, William P. Kelly, M. Stumpf, 2010, BMC Bioinformatics.

ABC in systems biology

Michael P. H. Stumpf, Juliane Liepe, 2018 .

StochDecomp - Matlab package for noise decomposition in stochastic biochemical systems

Michael P. H. Stumpf, Anna Gambin, Michal Komorowski, 2014, Bioinform..

Inferring extrinsic noise from single-cell gene expression data using Approximate Bayesian Computation

Michael P. H. Stumpf, Oleg Lenive, Paul Kirk, 2015 .

Exactly solvable models of stochastic gene expression

Michael P. H. Stumpf, Lucy Ham, David Schnoerr, 2020, bioRxiv.

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

Paul D. W. Kirk, Michael P. H. Stumpf, Oleg Lenive, 2015, bioRxiv.

A Graph Theoretical Approach to Data Fusion

Michael P. H. Stumpf, Justina Žurauskienė, Paul Kirk, 2015 .

Information Processing by Simple Molecular Motifs and Susceptibility to Noise

Michael P. H. Stumpf, Oleg Lenive, Siobhan McMahon, 2015 .

Maximizing the Information Content of Experiments in Systems Biology

Michael P. H. Stumpf, Sarah Filippi, Juliane Liepe, 2013, PLoS Comput. Biol..

Turing patterns are common but not robust

Michael P. H. Stumpf, David Schnoerr, Mark Isalan, 2018, bioRxiv.

Handbook of Statistical Systems Biology: Stumpf/Handbook of Statistical Systems Biology

Mark Girolami, Michael P. H. Stumpf, David J. Balding, 2011 .

SYSBIONS: nested sampling for systems biology

Rob Johnson, Paul D. W. Kirk, Michael P. H. Stumpf, 2015, Bioinform..

Faculty Opinions recommendation of Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation.

Michael P. H. Stumpf, 2014 .

StochSens - matlab package for sensitivity analysis of stochastic chemical systems

Michael P. H. Stumpf, Justina Zurauskiene, Michal Komorowski, 2012, Bioinform..

Parametric and Non-parametric Gradient Matching for Network Inference

Michael P. H. Stumpf, Fei He, Leander Dony, 2018, bioRxiv.

GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation

Fei He, Michael P. H. Stumpf, Jonathan Ish-Horowicz, 2019, bioRxiv.

Model Selection in Systems Biology Depends on Experimental Design

Paul D. W. Kirk, Michael P. H. Stumpf, Tina Toni, 2014, PLoS Comput. Biol..

SNPSTR: a database of compound microsatellite-SNP markers

Michael P. H. Stumpf, I. Agrafioti, M. Stumpf, 2007, Nucleic Acids Res..

GPU accelerated biochemical network simulation

Michael P. H. Stumpf, Xia Sheng, Juliane Liepe, 2011, Bioinform..

Using Likelihood-Free Inference to Compare Evolutionary Dynamics of the Protein Networks of H. pylori and P. falciparum

Carsten Wiuf, Michael P. H. Stumpf, Sylvia Richardson, 2007, PLoS Comput. Biol..

Tutorial on ABC rejection and ABC SMC for parameter estimation and model selection

Michael P. H. Stumpf, Tina Toni, M. Stumpf, 2009, 0910.4472.

Inference of temporally varying Bayesian Networks

Michael P. H. Stumpf, Thomas Thorne, M. Stumpf, 2012, Bioinform..

Coalescent models for developmental biology and the spatio-temporal dynamics of growing tissues

Michael P. H. Stumpf, Patrick Smadbeck, M. Stumpf, 2015, bioRxiv.

Generating confidence intervals on biological networks

Michael P. H. Stumpf, Thomas Thorne, M. Stumpf, 2007, BMC Bioinformatics.

PEITH(Θ): perfecting experiments with information theory in Python with GPU support

Michael P. H. Stumpf, Sarah Filippi, Scott Ward, 2018, Bioinform..

From qualitative data to quantitative models: analysis of the phage shock protein stress response in Escherichia coli

Michael P. H. Stumpf, Tina Toni, Maxime Huvet, 2011, BMC Systems Biology.

Which species is it? Species-driven gene name disambiguation using random walks over a mixture of adjacency matrices

Michael P. H. Stumpf, Wendy Filsell, Nathan Harmston, 2012, Bioinform..

On Industrial Strength Bio-design Automation

Michael P. H. Stumpf, Eyck Jentzsch, Wolf-Ekkehard Matzke, 2014, ICTERI.

Multi-model inference of network properties from incomplete data

Michael P. H. Stumpf, Thomas Thorne, 2006, J. Integr. Bioinform..

Reverse Engineering Under Uncertainty

Paul Kirk, Michael P. H. Stumpf, Daniel Silk, 2016 .

Statistical inference of the time-varying structure of gene-regulation networks

Michael P. H. Stumpf, Sophie Lèbre, Gaëlle Lelandais, 2010, BMC Systems Biology.

The Design Principles of Discrete Turing Patterning Systems

Michael P. H. Stumpf, Heike Siebert, Elisa Tonello, 2020, bioRxiv.

Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds

Michael P. H. Stumpf, M. Stumpf, 2019, bioRxiv.

Control mechanisms for stochastic biochemical systems via computation of reachable sets

Michael P. H. Stumpf, Eszter Lakatos, 2016 .

Empirical Bayes Meets Information Theoretical Network Reconstruction from Single Cell Data

Michael P. H. Stumpf, Ann C. Babtie, Thalia E. Chan, 2018 .

Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds

Michael P. H. Stumpf, 2020, Journal of the Royal Society Interface.

Inference of Signalling Pathway Models

Michael P. H. Stumpf, Juliane Liepe, Tina Toni, 2011 .

Great cities look small

Mauricio Barahona, Michael P. H. Stumpf, Aaron Sim, 2015, Journal of The Royal Society Interface.

Nonidentifiability of the Source of Intrinsic Noise in Gene Expression from Single-Burst Data

Michael P. H. Stumpf, Piers J. Ingram, Jaroslav Stark, 2008, PLoS Comput. Biol..

Considerate approaches to constructing summary statistics for ABC model selection

Michael P. H. Stumpf, Sarah Filippi, Chris P. Barnes, 2012, Stat. Comput..

Statistical Model Selection Methods Applied to Biological Networks

Carsten Wiuf, Michael P. H. Stumpf, Piers J. Ingram, 2005, Trans. Comp. Sys. Biology.

Simulation-based model selection for dynamical systems in systems and population biology

Michael P. H. Stumpf, Tina Toni, M. Stumpf, 2009, Bioinform..

Derivative processes for modelling metabolic fluxes

Paul D. W. Kirk, Michael P. H. Stumpf, Justina Zurauskiene, 2014, Bioinform..

Network inference from single-cell data using multivariate information measures

Michael P. H. Stumpf, Ann C. Babtie, Thalia E. Chan, 2017 .

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

Erika Cule, Paul D. W. Kirk, Michael P. H. Stumpf, 2010, Bioinform..

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

Paul D. W. Kirk, Michael P. H. Stumpf, Ann C. Babtie, 2016, Bioinform..

Graph spectral analysis of protein interaction network evolution

Michael P. H. Stumpf, Thomas Thorne, M. Stumpf, 2012, Journal of The Royal Society Interface.

Statistical analysis of network data and evolution on GPUs: High-performance statistical computing

Michael P. H. Stumpf, Thomas Thorne, 2012 .

Handbook of Statistical Systems Biology

Mark Girolami, Michael P. H. Stumpf, David J. Balding, 2011 .

A Network Analysis Primer

Carsten Wiuf, Michael P. H. Stumpf, M. Stumpf, 2009 .

Model comparison via simplicial complexes and persistent homology

Michael P. H. Stumpf, Sean T. Vittadello, M. Stumpf, 2020, Royal Society Open Science.

Inferring better gene regulation networks from single-cell data

Michael P. H. Stumpf, M. Stumpf, 2021 .

On optimal kernel in ABC SMC

Michael P. H. Stumpf, Sarah Filippi, Chris P. Barnes, 2011 .

A parameter-free model selection criterion based on steady-state coplanarity

Kenneth L. Ho, Heather A. Harrington, Michael P. H. Stumpf, 2011 .

Statistical and evolutionary analysis of biological networks

Carsten Wiuf, Michael P. H. Stumpf, M. Stumpf, 2009 .

Species-Area Relations in Tropical Forests

Michael P. H. Stumpf, Robert M. May, R. May, 2000, Science.

Cellular population dynamics control the robustness of the stem cell niche

Paul D. W. Kirk, Michael P. H. Stumpf, Adam L. MacLean, 2015, Biology Open.

Language's place in nature

Michael P. H. Stumpf, M. Stumpf, 2001 .

Learning regulatory models for cell development from single cell transcriptomic data

Michael P. H. Stumpf, Ann C. Babtie, Thalia E. Chan, 2017 .

Comprar Handbook Of Statistical Systems Biology | Michael P. H. Stumpf | 9780470710869 | Wiley

Mark Girolami, Michael P. H. Stumpf, David J. Balding, 2011 .