About the project
Many biomedical studies profile patients using multiple modalities (e.g. various omics and imaging technologies), but there is a lack of knowledge-driven approaches of how to best use these multimodal datasets together to improve patient management. Further, most real-world healthcare datasets contain high degrees of missing data, together with high-dimensional and correlated genomic and molecular features, which hinder the use of standard ML approaches in treatment response modelling and prediction. This project will develop new knowledge-driven ML methods to make the most of these incomplete datasets for personalized treatment recommendations using logic-based decision rules and decision systems, where biological and clinical knowledge guides the search space exploration. We will develop ML approaches that allow data integration even in the presence of data missing not at random, enabling prediction without imputation but by exploiting the modalities redundancy structure. Therefore, a key theme in this project is redundancy in the heterogeneous data sources.
Project team
Associate Professor Valeria Vitelli, University of Oslo, Institute of Basic Medical Sciences
Researcher Basil Ell, University of Oslo, Department of Informatics
Professor Tero Aittokallio, University of Oslo, Institute of Basic Medical Sciences
- Professor Riccardo De Bin, University of Oslo, Department of Mathematics
- Professor Sylvia Richardson, University of Cambridge, MRC Biostatistics Unit
- PhD Fellow Christoffer Lingjærde, University of Oslo, Institute of Basic Medical Sciences