Sample-specific cooperative learning integrating heterogeneous radiomics and pathomics data

Multi-omics analysis offers unparalleled insights into the interlinked molecular interactions that govern the underlying biological processes. In the era of big data, driven by the emergence of high-throughput technologies, it is possible to gain a more comprehensive and detailed understanding of complex systems. Nevertheless, the challenges lie in developing methods to effectively integrate and analyze this wealth of data. This challenge is even more apparent when the type of -omics data (e.g., pathomics) lacks pixel-to-pixel or region-to-region correspondence across the population. A novel sample-specific cooperative learning framework is introduced, designed to adaptively manage diverse multi-omics data types, even when there is no direct correspondence between regions. The proposed framework is defined for both continuous and categorical outcomes, with theoretical guarantees based on finite samples. Model performance is demonstrated and compared with existing methods using real-world datasets involving proteomics and metabolomics, and radiomics and pathomics.