Bioinformatics is inherently interdisciplinary and draws on computer science, math and statistics to enable discoveries in molecular biology data. Of particular interest is the application of AI and machine learning (ML) that promises to leverage the information hidden in massive data sets that are currently being generated by genome sequencing based technologies.

Mikael Boden and his group’s research aims to develop, investigate and apply the theory and practice of AI, statistical ML, data mining and probabilistic methods to understand and resolve a range of open problems in genomics, molecular and systems biology. There is a need to use scientific expertise to distinguish patterns in extremely high-dimensional feature spaces that are biologically meaningful from those that are artefacts of the data generation process. Moreover, the joint analysis of multiple, complementary data sets, enables the integration and assembly of information that is inaccessible from data sets when viewed in isolation.

The Boden group is actively collaborating with scientists in genomics, epigenetics and protein science, and is regularly developing methods, embedding ML and AI algorithms, that are then used by the scientific community.

Project members

Associate Professor Mikael Boden

Associate Professor
School of Chemistry and Molecular Biosciences
Affiliate Research Fellow
Institute for Molecular Bioscience