Xuejian Rong, Chucai Yi, Yingli Tian
Unambiguous Text Localization, Retrieval, and Recognition for Cluttered Scenes
IEEE transactions on pattern analysis and machine intelligence
integrative multiomics analysis provides deeper insight enables better realistic modeling underlying biology causes diseases single omics analysis although several integrative multiomics analysis methods proposed demonstrated promising results integrating distinct omics datasets inconsistent distribution different omics data caused technology variations poses challenge paired integrative multiomics methods addition existing discriminant analysisbased integrative methods effectively exploit correlation consistent discriminant structures necessitating compromise correlation discrimination using methods herein present panomics discriminant analysis panda joint discriminant analysis method seeks omicsspecific discriminant common spaces jointly learning consistent discriminant latent representations omics panda jointly maximizes betweenclass minimizes withinclass omics variations common space simultaneously models relationships among omics consistency representation crossomics correlation levels overcoming need compromise discrimination correlation existing integrative multiomics methods consistency representation learning incorporated objective function panda method seeks common discriminant space minimize differences distributions among omics lead robust latent representations methods inconsistency different omics compared panda 10 stateoftheart multiomics data integration methods using simulated realworld multiomics datasets found panda consistently outperformed providing meaningful discriminant latent representations panda implemented using r matlab codes available httpsgithubcomwulabmdapanda