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.
The Mathematical and Statistical Ecology study track is taught by the faculty of the Biomathematics Group and the Environmental and Ecological Statistics Group. Researchers in these groups have deep background in mathematics and statistics, as well as long experience in applying these methods to ecology and environmental sciences. Their research spans a wide range from mathematical modelling and theoretical and computational statistics to key questions in ecology, and to practical problems of environmental management and risk assessment.
A main focus of research in the Biomathematics Group is how ecological interactions drive evolutionary changes: Members of the group are among the founders of adaptive dynamics, one of the main mathematical frameworks to study evolution by natural selection. The Ecological Statistics Group is a strong and vibrant hub with two interconnected goals, the development of new statistical methods for ecological and environmental data and researching ecological and environmental change.
We put a large emphasis on mechanistic modelling, i.e., on deriving models from the underlying biological processes. Going beyond ecology, transferable skills in model construction and analysis may be used also in other areas. Models provide creative insight and understanding and, importantly, are at the core of modern Bayesian statistics. Through the combined mathematical and statistical expertise available in this study track, students can master the entire process of model building, analysis, fitting to data and arriving at new knowledge or expert advice for management. One can also specialise and go deeper into one of the subfields in mathematical or statistical ecology.