Machine learning for graph-structured data: Understanding complex biological systems

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Many complex biological and man-made systems can efficiently be described as graphs (networks) describing the elements involved and their mutual relations. Examples include, but are not limited to: the human brain, protein molecules, drugs and chemical compounds, power grids, and human transport networks. Network science has been created to complement the abstract framework offered by graph theory (a branch of mathematics), making it useful to study complex networked systems and their temporal (over time) evolution. However, modeling the behaviour of such systems down to microscopic details is not always possible, first and foremost because their behaviour and function usually emerges in a highly non-trivial way from the interactions of relatively simple components. Recently proposed machine learning methods based on deep neural networks (which are good at solving problems that have been difficult to solve historically using computers) allow predictions to be made based on graph-structured data. This intriguing approach is challenged by the fact that, usually, there exists a non-trivial temporal dependency between the observations representing the evolution of the system. Unfortunately, most statistical methods for predicting changes in behaviour assume independent inputs that have no temporal ordering. Moreover, such methods must typically be supervised in their learning and reliable supervision is difficult to obtain in many applications aiming at predicting anomalies or changes in the behaviour of complex systems. My long-term goal is to develop unsupervised machine learning methods for predicting and reacting to behaviour changes in complex networked systems by analyzing graph-structured data having temporal correlations. In the short-term, my research will consist of three (3) interrelated objectives. (1) First, I will design autoregressive and state-space models (that support forecasting expected future behaviour based on previously seen behaviour) for graph-structured data. With this knowledge, my team will tackle two applications of great societal importance. The first is in computational neuroscience. (2) Together with my students, I will design novel data-driven models for predicting and reacting to the onset of epileptic seizures in drug-resistant patients by analyzing brain recordings in the form of intracranial EEGs (brain activity scans). The second application is in structural biology. (3) We will also design other novel data-driven models to process a sequence of graphs representing the evolution of protein-protein and protein-ligand complexes over time (to help understand important functions in our bodies). This should ultimately lead to the design of more stable and effective drugs. My HQP involved in the proposed research will gain significant expertise in advanced machine learning methods and their application for graph-structured data, skills that have become strategic both in academia and industry.