A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects

@inproceedings{Dundar2014ANB,
  title={A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects},
  author={Murat Dundar and Ferit Akova and Halid Ziya Yerebakan and Bartek Rajwa},
  booktitle={BMC Bioinformatics},
  year={2014}
}
Flow cytometry (FC)-based computer-aided diagnostics is an emerging technique utilizing modern multiparametric cytometry systems.The major difficulty in using machine-learning approaches for classification of FC data arises from limited access to a wide variety of anomalous samples for training. In consequence, any learning with an abundance of normal cases and a limited set of specific anomalous cases is biased towards the types of anomalies represented in the training set. Such models do not… CONTINUE READING
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