This article discusses the importance of identifying key molecules and mechanisms involved in the pathophysiology of HIRI and how machine learning techniques can aid in this process. The authors review publicly available data and use a combination of machine learning algorithms and analytical tools to identify and validate disease models and key molecules involved in HIRI.
