期刊
NATURE MACHINE INTELLIGENCE
卷 2, 期 10, 页码 619-628出版社
NATURE PORTFOLIO
DOI: 10.1038/s42256-020-00232-8
关键词
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资金
- March of Dimes Prematurity Research Center at Stanford [22-FY18-808]
- Bill and Melinda Gates Foundation [OPP1112382, OPP1189911, OPP1113682]
- Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University
- Robertson Foundation
- NIH [R21DE02772801, 1R01HL13984401A1, R35GM138353, K23GM111657]
- American Heart Association [18IPA34170507, 19PABHI34580007]
- Food and Drug Administration (FDA) [HHSF223201610018C]
- Burroughs Wellcome Fund [1019816]
- National Institutes of Health (NIH) [R21DE02772801, R01AG058417, R01HL13984401, R61NS114926]
- US FDA [HHSF223201610018C]
- Stanford Immunology Training Grant [5 T32 AI07290-33]
- Doris Duke Charitable Foundation [2018100]
- Stanford Maternal and Child Health Research Institute
- Bill and Melinda Gates Foundation [OPP1189911, OPP1112382] Funding Source: Bill and Melinda Gates Foundation
Recent advances have increased the dimensionality and complexity of immunological data. The authors developed a machine learning approach to incorporate prior immunological knowledge and applied it on clinical examples and a simulation study. The approach may be useful for high-dimensional datasets in clinical settings where the cohort size is limited. The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.
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