Machine Learning of Discriminative Gate Locations for Clinical Diagnosis.

ABSTRACT: High-throughput single-cell cytometry technologies have significantly improved our understanding of cellular phenotypes to support translational research and the clinical diagnosis of hematological and immunological diseases. However, subjective and ad hoc manual gating analysis does not adequately handle the increasing volume and heterogeneity of cytometry data for optimal diagnosis. Prior work has shown that machine learning can be applied to classify cytometry samples effectively. However, many of the machine learning classification results are either difficult to interpret without using characteristics of cell populations to make the classification, or suboptimal due to the use of inaccurate cell population characteristics derived from gating boundaries. To date, little has been done t

SUBMITTER: Ji D 

PROVIDER: S-EPMC7079150 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature