Giuseppe Palmisano

STEP-PTM enables integrated quantitative analysis of proteome, metabolome, and multiple PTM classes from single biological samples using a modular TMT-multiplexed workflow.

This paper introduces GPMAW glyco-search, a workflow for high-confidence identification of intact sialylated N-glycopeptides using TiO2 enrichment, dual LC-MS/MS, and interactive spectrum validation, achieving over 2800 identifications in human plasma.

This paper applies fractal analysis to MALDI-TOF MS data for glioblastoma diagnosis, showing fractals can improve machine learning models but still underperform compared to traditional feature extraction.

This study uses MALDI-TOF MS and machine learning to identify serum biomarkers for astrocytoma diagnosis, achieving 94.5% accuracy in distinguishing GBM from healthy controls.

The paper analyzes RNAseq data from ocular cells of infants exposed to Zika virus prenatally, revealing gene expression changes in both symptomatic and asymptomatic cases.

This paper describes an optimized impression cytology method to obtain viable ocular cells from babies with Congenital Zika Syndrome, enabling transcriptomic and proteomic analyses.

This paper introduces a mass spectrometry-based strategy called TiCPG to simultaneously analyze proteome and three PTMs in cytokine-stimulated INS-1E cells, revealing extensive PTM regulation in beta-cell apoptosis pathways.