Precision medicine aims to tailor treatment to the individual, improving medical outcomes and quality of life. Realizing this vision requires understanding how disease mechanisms and drug responses vary across patients. Advances in molecular profiling have enabled detailed measurement of genetic, epigenetic, spatial, and imaging features at multiple biological scales, from single cells to tissues. These rich and complementary data promise insight into the drivers of human disease where individual data layers have often provided an incomplete picture. Increasingly, studies span multiple measurement modalities and scales, presenting both opportunities and challenges. Central among these is how to combine data types to uncover actionable biology. This review surveys computational strategies for analyzing multimodal and multiscale datasets, distinguishing between approaches that treat each modality independently and those that perform true integrative modeling. We highlight emerging methods, with a focus on oncology, where these tools are helping to reveal mechanisms and guide therapeutic decisions.
Ozturk et al. (Fri,) studied this question.