Computational translation of genomic responses from experimental model systems to humans.

ABSTRACT: The high failure rate of therapeutics showing promise in mouse models to translate to patients is a pressing challenge in biomedical science. Though retrospective studies have examined the fidelity of mouse models to their respective human conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for inference of disease-associated human differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human biology from mouse datasets. We found that semi-supervised training of a

SUBMITTER: Brubaker DK 

PROVIDER: S-EPMC6343937 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature