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MultiPLIER: a transfer learning framework reveals systemic features of rare autoimmune disease

View ORCID ProfileJaclyn N. Taroni, Peter C. Grayson, Qiwen Hu, Sean Eddy, Matthias Kretzler, Peter A. Merkel, View ORCID ProfileCasey S. Greene
doi: https://doi.org/10.1101/395947
Jaclyn N. Taroni
Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USAChildhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Philadelphia, PA, USA
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  • ORCID record for Jaclyn N. Taroni
Peter C. Grayson
National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
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Qiwen Hu
Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
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Sean Eddy
National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
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Matthias Kretzler
Division of Nephrology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, MI, USADepartment of Computational Medicine and Bioinformatics, Michigan Medicine, Ann Arbor, MI, USA
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Peter A. Merkel
Division of Rheumatology and the Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
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Casey S. Greene
Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USAChildhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Philadelphia, PA, USAInstitute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USAInstitute of Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
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  • For correspondence: greenescientist@gmail.com
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SUMMARY

Unsupervised machine learning methods provide a promising means to analyze and interpret large datasets. However, most datasets generated by individual researchers remain too small to fully benefit from these methods. In the case of rare diseases, there may be too few cases available, even when multiple studies are combined. We sought to determine whether or not machine learning models could be constructed from large public data compendia and then transferred to small datasets for subsequent analysis. We trained models using Pathway Level Information ExtractoR (PLIER) over datasets of different types and scales. Models constructed from large public datasets were i) more detailed than those constructed from individual datasets; ii) included features that aligned well to important biological factors; iii) transferrable to rare disease datasets where the models describe biological processes related to disease severity more effectively than models trained within those datasets.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 20, 2018.
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MultiPLIER: a transfer learning framework reveals systemic features of rare autoimmune disease
Jaclyn N. Taroni, Peter C. Grayson, Qiwen Hu, Sean Eddy, Matthias Kretzler, Peter A. Merkel, Casey S. Greene
bioRxiv 395947; doi: https://doi.org/10.1101/395947
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MultiPLIER: a transfer learning framework reveals systemic features of rare autoimmune disease
Jaclyn N. Taroni, Peter C. Grayson, Qiwen Hu, Sean Eddy, Matthias Kretzler, Peter A. Merkel, Casey S. Greene
bioRxiv 395947; doi: https://doi.org/10.1101/395947

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