Multi-omics, multi-modal, multi-scale data fusion for precision medicine
Vast amounts of biomedical data are now routinely available for patients ranging from sequencing of tissues to liquid biopsies. In addition, new computational tools for quantitatively analyzing radiographic images are now available. Multi-scale data is now available for complex diseases at molecular, cellular and tissue scale to establish a more comprehensive view of key biological processes. Intra and inter individual heterogeneities are often quoted as the main challenge for studying complex diseases. These heterogeneities exist at all scales, from microscopic to macroscopic. We develop multi-scale modeling approach to counter heterogeneity and uncover potentially untapped synergies between different data modalities by integrating information across spatial scales. Multi-scale modeling involves linking information from molecules, cells, tissues, and organs all the way to the organism and the population. We propose to use high dimensional molecular data with tissue scale image data to develop a statistical multi-scale modeling approach in the context of multi-modal & multi-scale modeling. Such modeling can contribute toward predicting diagnosis and treatment by revealing synergies and previously unappreciated relationships. Multi-scale modeling also can contribute to a more fundamental understanding of disease development and can reveal novel insights in how data at different scales are linked to each other.
