Jonathan Fiorentino

HERCULES predicts RNA-binding regions and mutation effects using a deep-learning framework combining language models and physicochemical descriptors.

PHANES provides a comprehensive, interpretable database of protein features for the human proteome, integrating sequence, structure, and expression data to support biological analysis and machine learning.

scRAPID-web predicts protein-RNA interactions from scRNA-seq data across eight organisms, offering GRN inference and visualization tools.

This study shows that LBR nucleoplasmic domains regulate X-chromosome solubility and nuclear organization during neural differentiation in mouse cells.

This study shows that reprogramming success varies by cell type and is influenced by the expression of genes from the original cell type, with some cells resisting reprogramming and causing developmental issues.

This paper introduces catGRANULE 2.0 ROBOT, an algorithm predicting protein LLPS at single amino acid resolution, showing high performance and experimental validation.

The paper characterizes the transcriptional dynamics of the mouse Anterior Visceral Enderm (AVE) during migration and identifies Ephrin- and Semaphorin-signalling as critical for its movement.

The paper benchmarks algorithms for inferring gene regulatory networks from single-cell RNA-seq data by evaluating their ability to capture topological properties and identify hubs.

This study develops scRAPID, a pipeline that predicts protein-RNA interactions from single-cell transcriptomic data, showing comparable or better performance than transcription factor-target interaction prediction.

PRALINE is a database that catalogs proteins and RNAs in biological condensates, their interactions, and the impact of single-nucleotide variants on condensate properties.

The paper develops an in vitro protocol to generate human Pharyngeal Endoderm cells and uses multi-omics analysis to reveal Retinoic Acid's role in their epigenetic and transcriptional development.