A neural network approach to determining cellular viability

John Quinn, Ram Achuthanandam, Peter J. Bugelski, Renold J. Capocasale, Paul W. Fisher, Moshe Kam, Leonid Hrebien

Research output: Contribution to journalConference articlepeer-review


Determination of cellular viability is a frequent goal of flow cytometry assays, and most published methods for creating boundaries that separate live, apoptotic, and dead cells are based on heuristics. We describe a method of determining these boundaries by training neural networks to learn the intensity patterns of a subset of cells with known viability, and then produce decision boundaries based on the networks measure of similarity. Five networks were studied and a Radial Basis Perceptron was found to be the most accurate. We have shown that these neural networks provide an objective rationale for classification using all available data.

Original languageEnglish (US)
Article number2.1.1
Pages (from-to)34-35
Number of pages2
JournalProceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC
StatePublished - 2005
Externally publishedYes
EventProceedings of the 2005 IEEE 31st Annual Northeast Bioengineering Conference - Hoboken, NJ, United States
Duration: Apr 2 2005Apr 3 2005

All Science Journal Classification (ASJC) codes

  • General Chemical Engineering


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