Conference proceeding
A neural network approach to determining cellular viability
Proceedings of the IEEE 31st Annual Northeast Bioengineering Conference, 2005
2005
Abstract
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.
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Details
- Title
- A neural network approach to determining cellular viability
- Creators
- J Quinn - Drexel UniversityR Achuthanandam - Drexel UniversityP.J BugelskiR.J CapocasaleP.W FisherM KamL Hrebien
- Publication Details
- Proceedings of the IEEE 31st Annual Northeast Bioengineering Conference, 2005
- Conference
- IEEE 31st Annual Northeast Bioengineering Conference, 2005, 31st
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Other Identifier
- 991019182655904721