To date most successful machine learning techniques for the analysis of complex interdisciplinary data predominantly use significant amounts of vectorial measurements as input to a statistical system. The domain expert knowledge is often only used in data preprocessing and the subsequently trained technique appears as a black-box, which is difficult to interpret or judge and rarely allows insight into the underlying natural process. However, in many bio-medical applications the underlying biological process is complex and the amount of measurements is limited due to the costs and inconvenience for the patient.
Our Marie Curie project, titled ‘Learning in the Space of Dynamic Models for Adrenal Steroidogenesis (LeSoDyMAS)’, is a joint project between the School of Computer Science and the Centre for Endocrinology, Diabetes and Metabolism at the University of Birmingham. Further combining the expertise of an industrial partner, the University of Sheffield and Warwick in order to tackle the above mentioned problems. We started to tackle the above mentioned problems by combining the forces of Computer Science, Medicine, Engineering, Applied Mathematics and pharmaceutical industry to work on high impact bio-medical problems. We are aiming at the design of machine learning methods which allow the principled integration of expert knowledge for example in form of dynamic models.