Dr Frank DondelingerLecturer in Biostatistics
I am a Lecturer in Biostatistics at Lancaster University in the CHICAS (Combining Health Information, Computation And Statistics) group of the Lancaster Medical School. My research interests include Bayesian models for drug and treatment response, information sharing, transfer learning and group mapping for transferring knowledge from in vitro to in vivo datasets, network reconstruction from time-series and interventional data, efficient inference in complex high-dimensional Bayesian models, and parameter inference in models of biological systems.
My research focuses on applying machine learning techniques to practical problems in biological and biomedical research. I am particularly interested in problems involving molecular data, such as gene expression or genomic studies. While working at The Netherlands Cancer Research Institute, I learned about the biology of cancer and became interested in using hierarchical regression models for prediction on heterogeneous datasets, specifically in the context of drug response prediction.
Recently, I have been working on methods for dealing with high-dimensional outcomes in longitudinal datasets, with the goal of improving risk prediction and inference in these challenging models. I also work on multi-task regression methods for microbiome biology.
PhD Supervision Interests
Please contact me if you are interested in pursuing a PhD in machine learning or statistical modelling for molecular biomedicine. Potential PhD topics that I am currently offering are: 1) Multi-task regression for detecting global effects on the human microbiome. 2) Machine learning for drug selection in personalized cancer medicine. 3) Variational parameter inference in statistical models with intractable likelihoods.
Cell cycle synchronisation of Trypanosoma brucei by centrifugal counter-flow elutriation reveals the timing of nuclear and kinetoplast DNA replication
Benz, C., Dondelinger, F., McKean, P.G., Urbaniak, M.D. 14/12/2017 In: Scientific Reports. 7, 10 p.
Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study
Staedler, N., Dondelinger, F., Hill, S., Akbani, R., Lu, Y., Mills, G., Mukherjee, S. 15/09/2017 In: Bioinformatics. 33, 18, p. 2890-2896. 7 p.
DSI: Data Science of the Natural Environment
16/04/2018 → 15/04/2023
BMC Psychiatry (Journal)
PLoS ONE (Journal)