Cargando…
Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy
For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before di...
Autores principales: | Barone, Sierra M, Paul, Alberta GA, Muehling, Lyndsey M, Lannigan, Joanne A, Kwok, William W, Turner, Ronald B, Woodfolk, Judith A, Irish, Jonathan M |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8370768/ https://www.ncbi.nlm.nih.gov/pubmed/34350827 http://dx.doi.org/10.7554/eLife.64653 |
Ejemplares similares
-
Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy
por: Barone, Sierra M., et al.
Publicado: (2020) -
Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells
por: Leelatian, Nalin, et al.
Publicado: (2020) -
Discrete T-Cell and Inflammatory Profiles in the Blood Define Pulmonary Phenotypes After COVID-19 Illness.
por: Canderan, Glenda, et al.
Publicado: (2023) -
T-bet+ Memory B Cells Link to Local Cross-Reactive IgG upon Human Rhinovirus Infection
por: Eccles, Jacob D., et al.
Publicado: (2020) -
Survivors Of Severe COVID-19 With Long-Haul Respiratory Symptoms Display Enhanced Activation of Circulating T Cells
por: Canderan, Glenda, et al.
Publicado: (2022)