Computational translation of genomic responses from experimental model systems to humans
Name
journal.pcbi.1006286.pdf
Size
2.68 MB
Format
Adobe PDF
Checksum (MD5)
e42c6d84e5422f646a13bab491f3f5d7
Author(s)
Haigis, Kevin M.
Brubaker, Douglas
Proctor, Elizabeth A
Lauffenburger, Douglas A
Date Issued
January 2019
Journal
PLOS Computational Biology
Publisher
Public Library of Science
Citation
Brubaker, Douglas K., Elizabeth A. Proctor, Kevin M. Haigis, and Douglas A. Lauffenburger. “Computational Translation of Genomic Responses from Experimental Model Systems to Humans.” Edited by Richard A. Bonneau. PLOS Computational Biology 15, no. 1 (January 10, 2019): e1006286. © 2019 Brubaker et al.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1006286
