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Personal profile

My research in a nutshell

My aim is to improve biological knowledge about and diagnostics for human diseases (particularly early and late life). I combine the disciplines of data science (my original background) and molecular biology and work on data science approaches for biomedical 'big data'. This involves statistical, computational, bioinformatical and other techniques applied to a combination of studies that each measure large numbers of genes, proteins, for different patient groups. I deduct from these analyses new mechanisms in the human biological response to disease, as well as genes or proteins that can diagnose or predict disease.



The main thrust of my teaching activities is to convey essential data science knowledge to biomedical researchers, in order for them to be able to address the opportunities and challenges presented by biomedical 'big data'. 

-Course organiser: "Essential big-data science for biomedical researchers". Biomedical Sciences, 4th year  Honours Elective (course starts 2016)

-Lead practical demonstrator, Biomedical Sciences Year 3, School of Biomedical Sciences, The University of Edinburgh: “Genome-wide analysis for a neonatal microarray transcriptome study of bacterial infection”

-Lecturer: MSc HPC with Data Science, School of Physics, Edinburgh University, course: Data Analytics with HPC.

-Lecturer: Infectious Diseases Honours Programme, Biomedical Sciences.




Research Interests

Use and improvement of data science techniques to identify biological mechanisms and markers for human infections diseases from biomedical 'big data' (genomic, proteomic, metabolomic, clinical).


Dr. Thorsten Forster is a Statistical Research Fellow in Computational Biology in the Division of Pathway Medicine at the University of Edinburgh. He researches and teaches data science (statistics, computing, bioinformatics,...) for human biological responses to infectious disease pathogens, as measured by biomedical ‘big data’. He has worked on using classification algorithms on gene transcription data to identify bacterial infection in human neonates, on establishing that statistical meta-analysis models can be used for identifying consistent but low-expressed genes in even small sets of heterogeneous microarray data sets, and on high performance computing solutions to biomedical big data challenges.

Education/Academic qualification

Doctor of Philosophy (PhD), The University of Edinburgh

Award Date: 1 Jan 2014


  • QR180 Immunology
  • Statistical analysis
  • Large data sets
  • Interferon
  • Macrophages


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