Gaussian graphical models with applications to omics analyses
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9546
Abstract: Gaussian graphical models (GGMs) provide a framework for modeling conditional dependencies in multivariate data. In this tutorial, we provide an overview of GGM theory and a demonstration of various GGM tools in R. The mathematical… read more here.
Keywords: gaussian graphical; omics analyses; applications omics; models applications ... See more keywords
Annotated hypergraphs: models and applications
Sign Up to like & getrecommendations! Published in 2019 at "Applied Network Science"
DOI: 10.1007/s41109-020-0252-y
Abstract: Hypergraphs offer a natural modeling language for studying polyadic interactions between sets of entities. Many polyadic interactions are asymmetric, with nodes playing distinctive roles. In an academic collaboration network, for example, the order of authors… read more here.
Keywords: models applications; hypergraphs models; annotated hypergraphs;
Metasurface Antennas: New Models, Applications and Realizations
Sign Up to like & getrecommendations! Published in 2019 at "Scientific Reports"
DOI: 10.1038/s41598-019-46522-z
Abstract: This paper presents new designs, implementation and experiments of metasurface (MTS) antennas constituted by subwavelength elements printed on a grounded dielectric slab. These antennas exploit the interaction between a cylindrical surface wave (SW) wavefront and… read more here.
Keywords: antennas new; new models; applications realizations; metasurface antennas ... See more keywords
Federated Learning for Sparse Bayesian Models with Applications to Electronic Health Records and Genomics
Sign Up to like & getrecommendations! Published in 2022 at "Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing"
DOI: 10.1142/9789811270611_0044
Abstract: Federated learning is becoming increasingly more popular as the concern of privacy breaches rises across disciplines including the biological and biomedical fields. The main idea is to train models locally on each server using data… read more here.
Keywords: bayesian models; federated learning; models applications; learning sparse ... See more keywords