Cytometry masked autoencoder: An accurate and interpretable automated immunophenotyper

Single-cell cytometry data are crucial for understanding the role of the immune system in diseases and responses to treatment. However, traditional methods for annotating cytometry data face challenges in scalability, robustness, and accuracy. We propose a cytometry masked autoencoder (cyMAE), which...

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Vydáno v:	Cell reports. Medicine Ročník 5; číslo 11; s. 101808
Hlavní autoři:	Kim, Jaesik, Ionita, Matei, Lee, Matthew, McKeague, Michelle L., Pattekar, Ajinkya, Painter, Mark M., Wagenaar, Joost, Truong, Van, Norton, Dylan T., Mathew, Divij, Nam, Yonghyun, Apostolidis, Sokratis A., Clendenin, Cynthia, Orzechowski, Patryk, Jung, Sang-Hyuk, Woerner, Jakob, Ittner, Caroline A.G., Turner, Alexandra P., Esperanza, Mika, Dunn, Thomas G., Mangalmurti, Nilam S., Reilly, John P., Meyer, Nuala J., Calfee, Carolyn S., Liu, Kathleen D., Matthy, Michael A., Swigart, Lamorna Brown, Burnham, Ellen L., McKeehan, Jeffrey, Gandotra, Sheetal, Russel, Derek W., Gibbs, Kevin W., Thomas, Karl W., Barot, Harsh, Greenplate, Allison R., Wherry, E. John, Kim, Dokyoon
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Inc 19.11.2024 Elsevier
Témata:	Advanced Basic Science Algorithms automated gating deep learning Flow Cytometry - methods high-dimensional cytometry Humans immunophenotyping Immunophenotyping - methods machine learning mass cytometry representation learning Single-Cell Analysis - methods deep learning automated gating representation learning immunophenotyping high-dimensional cytometry mass cytometry machine learning
ISSN:	2666-3791, 2666-3791
On-line přístup:	Získat plný text
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