Machine learning for coping with uncertain biological data

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Machine learning and bioinformatics methods can be used to discover new patterns in data about complex biological processes (e.g. the biology of ageing). However, data in biology often involves uncertainty, and this uncertainty often leads to less reliable predictive models. Examples of such uncertainty in biology are the uncertainty about the precise functions of a gene or the uncertainty about whether or not a protein interacts with another protein. This inter-disciplinary PhD project will focus on developing new supervised machine learning methods for coping with uncertainty in biological data, particularly data about the properties and functions of ageing-related genes and proteins. The overall aim of the project is to develop new machine learning methods that not only improve predictive performance, but also lead to the discovery of new patterns or new insights about the biology of ageing.