Article

New interpretable machine learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy

2019-07-13

Abstract excerpt

High-dimensional single-cell cytometry is routinely used to characterize patient responses to cancer immunotherapy and other treatments. This has produced a wealth of datasets ripe for exploration but whose biological and technical heterogeneity make them difficult to analyze with current tools. We introduce a new interpretable machine learning method for single-cell mass and flow cytometry studies, FAUST, that ro...

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Literature Corpus work: c305555b-bcaa-5dcd-9ee4-358875de0de2
DOI: 10.1101/702118

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