Liepe, Juliane

A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation

Liepe, J., Kirk, P., Filippi, S., Toni, T., Barnes, C. P., & Stumpf, M. P. H. (2014) · Nature Protocols

Markov Chains and Monte Carlo MethodsGene Regulatory Network Analysis

Cited by 217

Maximizing the information content of experiments in systems biology

Liepe, J., Filippi, S., Komorowski, M., & Stumpf, M. P. H. (2013) · PLoS Computational Biology

Gene Regulatory Network AnalysisControl Systems and Identification

Cited by 170

ABC-SysBio approximate Bayesian computation in Python with GPU support

Liepe, J., Barnes, C., Cule, E., Erguler, K., Kirk, P., Toni, T., & Stumpf, M. P. H. (2010) · Bioinformatics

Markov Chains and Monte Carlo MethodsGene Regulatory Network Analysis

Cited by 136

Calibrating spatio-temporal models of leukocyte dynamics against in vivo live-imaging data using approximate Bayesian computation

Liepe, J., Taylor, H., Barnes, C. P., Huvet, M., Bugeon, L., Thorne, T., Lamb, J. R., Dallman, M. J., & Stumpf, M. P. H. (2012) · Integrative Biology

Zebrafish Biomedical Research ApplicationsMarkov Chains and Monte Carlo Methods

Cited by 33

Chemical crosslinking extends and complements UV crosslinking in analysis of RNA/DNA nucleic acid–protein interaction sites by mass spectrometry

Welp, L. M., Wulf, A., Chernev, A., Horokhovskyi, Y., Moshkovskii, S., Dybkov, O., Neumann, P., Pašen, M., Siraj, A., Raabe, M., et al. (2025) · Nucleic Acids Research

Advanced Proteomics Techniques and ApplicationsMass Spectrometry Techniques and Applications

Cited by 12