Principal component analysis of personalized biomolecular corona data for early disease detection

Abstract

Today, early disease detection (EDD) is a matter of more importance than ever in medicine. Upon interaction with human plasma, nanoparticles are covered by proteins leading to formation of a biomolecular corona (BC). As the protein patterns of patients with conditions differ from those of healthy subjects, current research into technologies based on the exploitation of personalized BC patterns could be a turning point for early disease detection. Here, we present a framework based on principal component analysis of large personalized BC datasets. We comment on how principal component analysis of personalized BC data is a fundamental step towards turning the output of basic research into fast, safe and inexpensive technologies with superior prediction ability than current methods.