Helena U. Zacharias

HIDE-Deconv improves deconvolution by analyzing cellular composition across multiple resolution levels, revealing hidden cellular remodeling in diseases.

HIDE improves cell-type deconvolution by incorporating a cell hierarchy, showing better performance in simulations and applied to breast cancer data.

DynaMiCs integrates single-cell and bulk transcriptomics data to improve cell-type deconvolution and map population changes between phenotypes.

Deconomix deconvolves bulk transcriptomics data to estimate cell compositions, background contributions, and cell-type-specific gene regulation.

CODEX models causal effects of perturbations on cancer cell lines using counterfactual reasoning, enabling in-silico exploration of drug combinations and genetic modifications.

ADTD improves estimation of cellular compositions from bulk transcriptomics data by adapting reference profiles to the molecular environment and identifying hidden contributions.

PriOmics integrates proteomics with other omics data using graphical models to better understand relationships between proteins and their modifications.

SpaCeNet identifies intracellular and intercellular molecular networks from spatial omics data by analyzing conditional independence relations between variables within and across cells.