Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions

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 poi...

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Veröffentlicht in:Nat Mach Intell
Hauptverfasser: Culos, Anthony, Tsai, Amy S., Stanley, Natalie, Becker, Martin, Ghaemi, Mohammad S., McIlwain, David R., Fallahzadeh, Ramin, Tanada, Athena, Nassar, Huda, Espinosa, Camilo, Xenochristou, Maria, Ganio, Edward, Peterson, Laura, Han, Xiaoyuan, Stelzer, Ina A., Ando, Kazuo, Gaudilliere, Dyani, Phongpreecha, Thanaphong, Marić, Ivana, Chang, Alan L., Shaw, Gary M., Stevenson, David K., Bendall, Sean, Davis, Kara L., Fantl, Wendy, Nolan, Garry P., Hastie, Trevor, Tibshirani, Robert, Angst, Martin S., Gaudilliere, Brice, Aghaeepour, Nima
Format: Artigo
Sprache:Inglês
Veröffentlicht: 2020
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Article
Online Zugang:https://ncbi.nlm.nih.gov/pmc/articles/PMC7720904/
https://ncbi.nlm.nih.gov/pubmed/33294774
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s42256-020-00232-8
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