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Found 6 papers, 0 papers with code
A Probabilistic Graph Coupling View of Dimension Reduction
Most popular dimension reduction (DR) methods like t-SNE and UMAP are based on minimizing a cost between input and latent pairwise similarities.
Identification of deregulated transcription factors involved in subtypes of cancers
We propose a methodology for the identification of transcription factors involved in the deregulation of genes in tumoral cells.
Fast Computation of Genome-Metagenome Interaction Effects
Our focus is on detecting interactions between groups of genetic and metagenomic markers in order to gain a better understanding of the complex relationship between environment and genome in the expression of a given phenotype.
Variational inference for sparse network reconstruction from count data
We adopt a different stance by relying on a latent model where we directly model counts by means of Poisson distributions that are conditional to latent (hidden) Gaussian correlated variables.
Methodology
Variational inference for probabilistic Poisson PCA
A typical example is the joint observation of the respective abundances of a set of species in a series of sites, aiming to understand the co-variations between these species.
Methodology
Sparsity by Worst-Case Penalties
We illustrate on real and artificial datasets that this accuracy is required to for the correctness of the support of the solution, which is an important element for the interpretability of sparsity-inducing penalties.
