Found In Translation: a machine learning model for mouse-to-human inference

• Published in: Nature methods Vol. 15; no. 12; pp. 1067 - 1073
• Main Authors: Normand, Rachelly, Du, Wenfei, Briller, Mayan, Gaujoux, Renaud, Starosvetsky, Elina, Ziv-Kenet, Amit, Shalev-Malul, Gali, Tibshirani, Robert J., Shen-Orr, Shai S.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.12.2018; Nature Publishing Group
• Subjects: 631/114/1305; 631/114/2397; 631/114/2415; Algorithms; Animal models; Animals; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Case-Control Studies; Clinical trials; Female; Gene expression; Gene Expression Profiling; Genes; Genomics - methods; Humans; Inflammatory Bowel Diseases - genetics; Learning algorithms; Life Sciences; Machine Learning; Male; Mathematical models; Medical research; Mice; Middle Aged; Predictions; Proteomics; Signal Transduction; Species; Statistical methods; Transcriptome; Translation; Translational Research, Biomedical
• ISSN: 1548-7091; 1548-7105
• DOI: 10.1038/s41592-018-0214-9

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.