Francesco Napolitano

The paper introduces tdFBA, a novel method for simulating circadian metabolism across organs, and shows it can recapitulate metabolic oscillations and perturbations in mice and humans.

The paper identifies small molecules that enhance gene expression in synthetic circuits by analyzing RNA-seq data and drug-induced profiles.

This paper introduces MODAE, a deep learning method that integrates cell-line and patient omics data to predict cancer survival and treatment response, preserving tumor heterogeneity.

This study uses AI to identify small molecules that induce breast cancer stem cell differentiation by training a deep neural network on gene expression data and validating results experimentally.

The paper identifies treatment-responsive genes in spatial transcriptomics data by analyzing the distance from injection sites in a murine brain dataset.

DECCODE identifies small molecules that enhance cell reprogramming and conversion using transcriptional data and drug profiles.

gene2drug prioritizes drugs based on their impact on transcriptional pathways of target genes, validated both computationally and experimentally for two drug repositioning cases.

The paper integrates drug chemical structures with transcriptional responses to identify noisy responses and a transcriptional signature for phospholipidosis.