This project will create intelligent systems for personalized and precise risk prediction and diagnosis of non-communicable diseases using multi-omics data, by developing, implementing and validating novel algorithms for structure learning and inference in large-scale, multi-organ causal Bayesian gene networks, based on computational methods that we have developed previously to infer, characterize and validate gene regulatory networks in complex diseases. Risk prediction algorithms are used to identify high-risk patients for early intervention to reduce the premature mortality from non-communicable diseases, but we have yet to discover models that can integrate the tens of thousands of data points that can be measured by omics technologies in a single drop of blood. To distinguish causation from correlation in multi-omics data we will develop non-parametric models for causal inference between tens of thousands of variables. To link multi-omics causal networks from multiple organs to disease states we will develop novel algorithms for large-scale Bayesian network structure learning. To create intelligent systems for risk prediction and diagnosis of non-communicable diseases that only use blood-based measurements we will develop efficient methods for inference in large-scale Bayesian networks. To implement a proof-of-concept application in cardiovascular medicine we will apply newly developed methods on a unique resource of multi-omics data from more than twenty thousand Nordic individuals to which the project will have privileged access. This project is an international and interdisciplinary collaboration in bioinformatics, systems biology, computer engineering, machine learning and cardiovascular medicine that will deliver a well-validated and scalable platform to create intelligent systems for personalized and precise risk prediction and diagnosis of non-communicable diseases that will identify high risk individuals more accurately than existing methods.
Project leader: Tom Luk Robert Michoel
Institution: Institutt for informatikk