Data from: A methylation-to-expression feature model for generating accurate prognostic risk scores and identifying disease targets in clear cell kidney cancer

Jeffrey A. Thompson & Carmen J. Marsit
Many researchers now have available multiple high-dimensional molecular and clinical datasets when studying a disease. As we enter this multi-omic era of data analysis, new approaches that combine different levels of data (e.g. at the genomic and epigenomic levels) are required to fully capitalize on this opportunity. In this work, we outline a new approach to multi-omic data integration, which combines molecular and clinical predictors as part of a single analysis to create a prognostic...
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