Time-Varying Clusters in Large-Scale Flow Cytometry

Jeremy Hyrkas, Daniel Halperin, Bill Howe

AAAI 2015 pp. 4022-4023

doi:10.1609/AAAI.V29I2.19067 /aaai/2015/hyrkas2015aaai-time/

Abstract

Flow cytometers measure the optical properties of particles to classify microbes. Recent innovations have allowed oceanographers to collect flow cytometry data continuously during research cruises, leading to an explosion of data and new challenges for the classification task. The massive scale, time-varying underlying populations, and noisy measurements motivate the development of new classification methods. We describe the problem, the data, and some preliminary results demonstrating the difficulty with conventional methods.

AAAI Semantic Scholar

Cite

Text

Hyrkas et al. "Time-Varying Clusters in Large-Scale Flow Cytometry." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I2.19067

Markdown

[Hyrkas et al. "Time-Varying Clusters in Large-Scale Flow Cytometry." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/hyrkas2015aaai-time/) doi:10.1609/AAAI.V29I2.19067

BibTeX

@inproceedings{hyrkas2015aaai-time,
  title     = {{Time-Varying Clusters in Large-Scale Flow Cytometry}},
  author    = {Hyrkas, Jeremy and Halperin, Daniel and Howe, Bill},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2015},
  pages     = {4022-4023},
  doi       = {10.1609/AAAI.V29I2.19067},
  url       = {https://mlanthology.org/aaai/2015/hyrkas2015aaai-time/}
}