Allows users to select characteristics from multi-omics data. CCAFS is a multi-view feature selection algorithm that uses the canonical correlation analysis (CCA) learned projective transformation. The method is able to integrate high-dimentional multi-omics data. It was applied to develop integrated models intending for predicting kidney renal clear cell carcinoma (KIRC) survival.
College of Information Sciences and Technologies, Pennsylvania State University University Park, PA, USA
CCAFS funding source(s)
Supported in part by the Center for Big Data Analytics and Discovery Informatics at the Pennsylvania State University and by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1 TR000127 and TR002014.