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• W2551633306 abstract "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 captures relationships in the data that represent highly disease-relevant functions." @default.
• W2551633306 created "2016-11-30" @default.
• W2551633306 creator A5012121180 @default.
• W2551633306 creator A5043239621 @default.
• W2551633306 date "2016-11-22" @default.
• W2551633306 modified "2023-09-24" @default.
• W2551633306 title "A METHYLATION-TO-EXPRESSION FEATURE MODEL FOR GENERATING ACCURATE PROGNOSTIC RISK SCORES AND IDENTIFYING DISEASE TARGETS IN CLEAR CELL KIDNEY CANCER" @default.
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• W2551633306 doi "https://doi.org/10.1142/9789813207813_0047" @default.
• W2551633306 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5177986" @default.
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• W2551633306 hasPublicationYear "2016" @default.
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