Format

Send to

Choose Destination
PLoS Comput Biol. 2019 Jan 10;15(1):e1006286. doi: 10.1371/journal.pcbi.1006286. eCollection 2019 Jan.

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

Author information

1
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America.
2
Cancer Research Institute, Beth Israel Deaconess Cancer Center and Department of Medicine, Harvard University Medical School, Boston, MA, United States of America.
3
Departments of Neurosurgery and Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania, United States of America.
4
Department of Biomedical Engineering, Pennsylvania State University, State College, Pennsylvania, United States of America.

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 neural network identified significantly more true human biological associations than interpreting mouse experiments directly. Evaluating the experimental design of mouse experiments where our model was most successful revealed principles of experimental design that may improve translational performance. Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches, combining mouse and human data for biological inference, provide the most accurate assessment of human in vivo disease processes. Finally, we proffer a delineation of four categories of model system-to-human "Translation Problems" defined by the resolution and coverage of the datasets available for molecular insight translation and suggest that the task of translating insights from model systems to human disease contexts may be better accomplished by a combination of translation-minded experimental design and computational approaches.

Conflict of interest statement

The authors have declared that no competing interests exist.

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center