María L. Rosano-González

scAmpi provides a clinical workflow for scRNA-seq analysis, extracting clinically relevant information and supporting drug treatment identification.

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.

The paper presents an automated workflow for in-silico drug response prediction in bladder cancer using multi-omic profiles, identifying personalized treatment options for most patients.