Biology and medicine are undergoing a transformation due to vast amounts of data generated by new technologies such as 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 the evolution of drug resistance. However, data alone are not sufficient to make sustained scientific or clinical progress. Powerful algorithms as well as computational methods are essential to translate these data into scientific discoveries and clinically useful information.
The Bioinformatics and Systems Medicine study track prepares students to the new era of biomedicine, where innovative computational approaches are required to interpret molecular and clinical data from patients or from disease-causing pathogens. The courses of this study track educate you to be an expert who can turn biomedical questions into appropriate challenges for computational data analysis and solve them.
The core of this study track connects directly to three research groups. The Bioinformatics and Evolution research group at the Faculty of Biological and Environmental Sciences and the Faculty of Science aims at understanding and predicting microbial evolution, including drug resistance, combining experimental evolution and data science. The research group Systems Biology of Drug Resistance in Cancer of the Faculty of Medicine develops machine learning methods to integrate molecular data with clinical information in pursuit of personalised medicine to combat drug resistant cell lines of cancers. The Algorithmic Bioinformatics group aims to provide a solid foundation for reliable and scalable methods to enable new breakthroughs based on high-throughput sequencing data.
The courses offered in this study track centre on algorithms and machine learning approaches for analysing molecular data and computational approaches that allow interpretation of high-throughput biomedical data. The curriculum also includes general algorithm and machine learning studies offered by the Master's Programmes in Computer Science, in Data Science, and in Genetics and Molecular Biosciences.
Biostatistics develops statistical methods for the life sciences, focusing 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. Our research projects include various biomedical disciplines such as epidemiology of complex diseases, genetic association studies, statistics in medicine and population genetics. The courses of the Biostatistics study track closely connect to this research. We collaborate, for example, with the Institute for Molecular Medicine Finland (FIMM), the Faculty of Medicine at University of Helsinki and the Finnish Institute for Health and Welfare.
In this study track, you specialise on mathematical and statistical methods in ecology. In a nutshell, ecology studies the distribution and abundance of species and their interactions with other species and the environment. As a scientific field, it has a key role in our ability to respond to the challenges posed by global change. In turn, mathematical and statistical modelling are essential for analyzing large ecological data sets. In the mathematical and statistical ecology study track, you will be trained on the latest methods and techniques of analysis in the field. The special courses include Bayesian statistics, statistics of experimental design in ecological studies, mathematics of infectious diseases, spatial ecology, stochastic population models, game theory, and adaptive dynamics. In addition to courses provided by the Life Science Informatics Master's programme, your methodological skills will be strengthened by courses from the Master's programme in Mathematics and Statistics and your ecological understanding will be strengthened by courses from the Master's programme in Ecology and Evolutionary Biology. Taken together, this study track offers exceptionally deep knowledge in mathematical and statistical ecology with transferable skills in modelling and data analysis.