Myrsini Kaforou

This paper introduces FS-PLS, a feature selection method that generates minimal disease signatures from omics data without requiring library size normalization, achieving smaller and comparable performance to existing methods.

The paper identifies a 5-transcript signature in blood samples that distinguishes bacterial from viral pneumonia with high accuracy.

The paper uses quantitative proteomics to compare EV cargo between M1 and M2 macrophages, identifying 132 differentially expressed proteins and their functional roles in immune responses.

This study compares transcriptomic profiles between human malaria patients and mouse models to identify which models best reflect human disease features.

This study stratifies Kawasaki Disease patients using transcriptomic and proteomic profiles, finding KD responses resemble bacterial infections but with distinct features suggesting alternative triggers.

The paper identifies reduced host transcriptomic signatures for tuberculosis using microarray data and validates them with digital PCR, showing high accuracy in distinguishing TB from other conditions and latent TB.

This study identifies distinct lipidomic changes in febrile children with bacterial vs. viral infections, showing potential for metabolic biomarkers to aid in clinical diagnosis.