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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Detalhes bibliográficos
Publicado no:Nat Mach Intell
Main Authors: 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
Formato: Artigo
Idioma:Inglês
Publicado em: 2020
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Article
Acesso em linha: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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