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

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 risk score
for clear cell renal cell carcinoma. The approach integrates data in
multiple ways and yet creates models that are relatively straightforward
to interpret and with a high level of performance. Furthermore, the
proposed process of data integration itself captures relationships in the
data that represent highly disease-relevant functions.