Found In Translation: a machine learning model for mouse-to-human inference. (December 2018)

• Record Type: Journal Article
• Authors: Normand, Rachelly; Du, Wenfei; Briller, Mayan; Gaujoux, Renaud; Starosvetsky, Elina; Ziv-Kenet, Amit; Shalev-Malul, Gali; Tibshirani, Robert; Shen-Orr, Shai
• Is Part Of: Nature methods. Volume 15:Number 12(2018)
• Page Start: 1067
• Page End: 1073
• Publication Date: 2018-12
• Subjects: Life sciences -- Methodology -- Periodicals; Life sciences -- Research -- Periodicals; Biology -- Methodology -- Periodicals; Biology -- Research -- Periodicals; 570.72
• DOI: 10.1038/s41592-018-0214-9
• Languages: English
• ISSNs: 1548-7091

Abstract

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. Here we present Found In Translation (FIT;http://www.mouse2man.org ), a statistical methodology that leverages 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 identified experimental conditions in which FIT predictions outperformed direct cross-species extrapolation from mouse results, increasing the overlap of differentially expressed genes by 20–50%. FIT predicted novel disease-associated genes, an example of which we validated experimentally. FIT highlights signals that may otherwise be missed and reduces false leads, with no experimental cost. The machine learning approach FIT leverages public mouse and human expression data to improve the translation of mouse model results to analogous human disease.