In theory, there is no difference between theory and practice. But, in practice, there is.

— Jan L.A. van de Snepscheut

Welcome to the Predictive Medicine and Analytics Lab!

  • Tampere University of Technology

  • Tampere University of Technology

  • Queen's University Belfast

We are living in an exciting time that sees a constant change in the way biology and medicine is conducted. One major reason for this change is due to technological progress that has been driven by the human genome project. As a result, we are nowadays in a position to measure unimaginable amounts of molecular, cellular and clinical data. Our group is embarking on the journey to make sense of these complex data.

Currently, we are especially interested in the analysis of sequencing data from next-generation sequencing experiments and gene expression data from DNA microarrays. In close collaboration with groups from biology and medicine we are studying disease related questions with a special focus on cancer. An ultimate goal of our research is to contribute to the deciphering of regulatory networks with respect to their reconstruction and functional analysis to shed light on causal mechanisms underlying complex diseases and pathological phenotypes.

In order to analyze high-dimensional and heterogeneous data sets efficiently, we engage in innovative interdisciplinary methodological research to develop and improve computational, statistical and mathematical methods. Specifically, we are interested in Bayesian statistics, exploratory data analysis, computational network theory, machine learning, high-dimensional statistics, and nonparametric statistical inference methods and their application to problems in systems and precision medicine.

A common denominator that is shared by all of our projects is that they are data-driven. This ensures the immediate applicability of our methods and their relevance to solve real world problems.

Quick facts about our work

Brief factual summary that highlights the work we are doing in our lab. More information can be found by following the links in the menu.




Journals: 102
Book chapters: 14
Conference articles: 28
Books: 11


Current research projects

Data Science
Computational Biology
Data visualization
Business Analytics



Comparative evaluation of gene set analysis approaches for RNA-Seq data, Rahmatallah, Emmert-Streib and Glazko, BMC Bioinformatics, accepted