Neural network analysis of digital flow cytometric data

Mahesh Godavarti, Jeffrey J. Rodriguez, Timothy A. Yopp, Georgina M. Lambert, David W. Galbraith

Research output: Contribution to conferencePaper

Abstract

In flow cytometry, the pulse waveform features measurable by current analog instruments are limited to the pulse integral, peak, and width. Digitization of the waveforms provides a means for the extraction of additional features, such as skewness, kurtosis, and Fourier properties. The introduction of additional features requires automated procedures for classification of biological particles. In this work, we implemented and evaluated neural network classification algorithms using derived, complex features, as well as using the raw, sampled data without feature extraction. The performance of the neural networks was compared with that of a more conventional means of classification in flow cytometry, the K-means clustering algorithm.

Original languageEnglish (US)
Pages2211-2216
Number of pages6
StatePublished - Dec 1 1995
EventProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) - Perth, Aust
Duration: Nov 27 1995Dec 1 1995

Other

OtherProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6)
CityPerth, Aust
Period11/27/9512/1/95

ASJC Scopus subject areas

  • Software

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  • Cite this

    Godavarti, M., Rodriguez, J. J., Yopp, T. A., Lambert, G. M., & Galbraith, D. W. (1995). Neural network analysis of digital flow cytometric data. 2211-2216. Paper presented at Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6), Perth, Aust, .