Assistant Professor Daifeng Wang, whose research focuses on manifold learning, had a paper titled “A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data” published in the prestigious journal, Nature Computational Science. The paper describes a tool he and his team developed called deepManReg, which uses cross-modal manifolds as a feature graph to regularize the classifiers for improving phenotype predictions and also for prioritizing the multi-modal features and cross-modal interactions for the phenotypes.
Congratulations, Daifeng!
November 2021 Manifold Learning paper in Communications Biology
January 2022 Deep Manifold Regularized Learning Model paper in Nature Computational Science