Sample-specific cooperative learning integrating heterogeneous radiomics and pathomics data

Music City Center 

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, we are well-positioned 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) lack pixel-to-pixel or region-to-region correspondence across the population.
In this paper, we introduce a novel sample-specific cooperative learning framework designed to adaptively manage diverse multi-omics data types, even when there is no direct correspondence between regions.
We outline this framework for both continuous and categorical outcomes with theoretical guarantees based on finite samples.
We demonstrate the model performance and compare with existing methods in two real world datasets with 1) proteomics and metabolomics; and 2) radiomics and pathomics.

Sample-Specific Prediction

Cooperative Learning

Multi-Omics Data