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Interpretable Single-Cell Set Classification with Kernel Mean Embeddings. (arXiv:2201.07322v1 [cs.LG])
Jan. 20, 2022, 2:10 a.m. | Siyuan Shan, Vishal Baskaran, Haidong Yi, Jolene Ranek, Natalie Stanley, Junier Oliva
cs.LG updates on arXiv.org arxiv.org
Modern single-cell flow and mass cytometry technologies measure the
expression of several proteins of the individual cells within a blood or tissue
sample. Each profiled biological sample is thus represented by a set of
hundreds of thousands of multidimensional cell feature vectors, which incurs a
high computational cost to predict each biological sample's associated
phenotype with machine learning models. Such a large set cardinality also
limits the interpretability of machine learning models due to the difficulty in
tracking how each …
More from arxiv.org / cs.LG updates on arXiv.org
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