Found in translation: a machine learning model for mouse-to-human inference
Tipus de documentText en actes de congrés
Data publicació2020
EditorBarcelona Supercomputing Center
Condicions d'accésAccés obert
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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.
CitacióNormand, R. Found in translation: a machine learning model for mouse-to-human inference. A: . Barcelona Supercomputing Center, 2020, p. 24-25.
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