Department
Biomedical Sciences
Document Type
Article
Publication Title
Nature Machine Intelligence
ISSN
2522-5839
Volume
2
Issue
10
DOI
10.1038/s42256-020-00232-8
First Page
619
Last Page
628
Publication Date
10-1-2020
Abstract
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.
Recommended Citation
Culos, A.,
Tsai, A. S.,
Stanley, N.,
Becker, M.,
Ghaemi, M.,
McIlwain, D. R.,
Fallahzadeh, R.,
Tanada, A.,
Nassar, H.,
Espinosa, C.,
Xenochristou, M.,
Ganio, E.,
Peterson, L.,
Han, X.,
Stelzer, I. A.,
Ando, K.,
Gaudilliere, D.,
Phongpreecha, T.,
Marić, I.,
Chang, A. L.,
Shaw, G. M.,
Stevenson, D. K.,
Bendall, S.,
Davis, K. L.,
Fantl, W.,
Nolan, G. P.,
Hastie, T.,
Tibshirani, R.,
&
Angst, M. S.
(2020).
Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions.
Nature Machine Intelligence, 2(10), 619–628.
DOI: 10.1038/s42256-020-00232-8
https://scholarlycommons.pacific.edu/dugoni-facarticles/716
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
