Florencio Pazos

The paper uses scRNA-seq data from non-diseased tissues to identify cell types associated with rare diseases using a computational framework called Cell4Rare, achieving an AUC of 0.71.

This study analyzes 21 years of Gene Ontology (GO) releases to understand its structural and annotation changes over time, identifying a shift toward stability around 2017.

This study identifies cell types associated with rare disease phenotypes using differential expression and co-expression analyses of scRNA-seq data from healthy tissues.

This study identifies DIDO3 binding at active chromatin and topological boundaries in mouse stem cells, linking it to chromatin organization and transcriptional regulation.

The paper predicts biological pathways of chemical compounds using a profile-inspired model based on fragment-based vectorial representations, achieving AUC scores between 0.74 and 0.90.