Search Results for author:
Found 2 papers, 2 papers with code
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
The interpretation of complex high-dimensional data typically requires the use of dimensionality reduction techniques to extract explanatory low-dimensional representations.
Stratification of patient trajectories using covariate latent variable models
To learn such a continuous disease score one could infer a latent variable from dynamic "omics" data such as RNA-seq that correlates with an outcome of interest such as survival time.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
