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 language||English (US)|
|Number of pages||2|
|Journal||Bioengineering, Proceedings of the Northeast Conference|
|State||Published - Dec 12 2005|
|Event||Proceedings of the 2005 IEEE 31st Annual Northeast Bioengineering Conference - Hoboken, NJ, United States|
Duration: Apr 2 2005 → Apr 3 2005
All Science Journal Classification (ASJC) codes