Study tracks
PAGE NO LONGER IN USE: The Master’s Programme in Life Science Informatics has four specialisation areas, so called study tracks: Bioin­form­at­ics and Systems Medicine, Bio­mathem­at­ics, Bio­s­tat­ist­ics and Eco-evol­u­tion­ary In­form­at­ics.

During the first autumn semester, each study track gives you an introductory course. At the beginning of the spring semester, you are assumed to have decided on your field of specialisation (study track).

Bioinformatics and Systems Medicine

Medicine is undergoing a transformation due to vast amounts of data generated from patients by new technologies such as the next generation sequencing and high-throughput phenotyping. This so called Big Data is creating great opportunities across fields ranging from personalised cancer therapies to detailed understanding of disease evolution. However, data alone are not sufficient to make scientific or clinical progress. Powerful algorithms as well as computational methods are essential to translate these data into scientific discoveries and clinical useful information.  Bioinformatics and Systems Medicine track prepares students to the new era of biomedicine, where innovative computational approaches are required to interpret  molecular and clinical data from patients.  This specialisation area educates you to be an expert who can turn biomedical questions into appropriate challenges for computational data analysis. For example, we offer biology tailored algorithms and machine learning approaches for analysing molecular data, and computational approaches that allow interpretation of high-throughput biomedical data obtained from patients. The curriculum also includes general algorithm and machine learning studies offered by the Master's Programmes in Computer Science, Data Science, and Genetics and Molecular Biosciences.


The Biomathematics study track offers an internationally unique program with seven regular courses in the area of mathematical ecology and evolution. We put large emphasis on mechanistic modelling, i.e., on deriving models from the underlying biological processes. Our focus on ecology provides fast access to the practice of modelling and provides transferable skills in model construction and analysis. Taking this research-oriented study track as the main line of studies is especially suitable for students who wish to continue in academia. To well-performing and motivated students, we offer publishable projects for the MSc thesis. Taking an introductory course on modelling is useful for everyone: Data make sense in the light of models. Models provide creative insight and understanding as well as are at the core of modern data analysis based on Bayesian statistics.


Biostatistics study track covers statistical methods for life sciences, with focus on how to make principled statistical inference in real life situations. The methods include, for example, variable selection, statistical clustering, large-scale inference and dimension reduction. Both Bayesian and other approaches are studied. The research applications cover collaborative topics in various biomedical disciplines such as epidemiology of complex diseases, genetic association studies, statistics in medicine and population genetics. Biostatistics track has a close collaboration with Institute for Molecular Medicine Finland (FIMM)

Eco-evol­u­tion­ary In­form­at­ics

Eco-evolutionary Informatics is a study track where you specialize to mathematical and statistical methods in ecology and evolutionary biology. Ecology studies the distribution and abundance of species, and their interactions with other species and the environment. Evolutionary biology studies processes supporting biodiversity on different levels from genes to populations and ecosystems. These sciences have a key role in responding to global environmental, biodiversity and sustainability challenges. Mathematical and statistical modelling, computer science and bioinformatics have an important role in ecology and evolutionary biology research and their applications. Our researchers and teachers have background in mathematics, statistics and computer science and long experience in applying these methods to ecology and evolutionary biology.