Found in translation: a machine learning model for mouse-to-human inference

Abstract

Cross-species differences form barriers to translational research that ultimately hinder the success of clinical trials, yet knowledge of species differences has yet to be systematically incorporated in the interpretation of animal models. We developed a machine learning model that leverages human and mouse public gene expression data to extrapolate the results of a new mouse experiment to expression changes in the equivalent human condition. We applied FIT to data from mouse models of 28 different human diseases and show it is able to identify 20-50% more human-relevant differentially expressed genes. FIT predicted novel disease-associated genes, an example of which we validated experimentally in Crohn’s patients. FIT highlights signals that may otherwise be missed and reduces false leads with no experimental cost. It is available both as an R package and as a web tool.