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

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 journal › Conference article › peer-review

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.

Original language	English (US)
Article number	2.1.1
Pages (from-to)	34-35
Number of pages	2
Journal	Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC
State	Published - 2005
Externally published	Yes
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

General Chemical Engineering

Cite this

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title = "A neural network approach to determining cellular viability",

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.",

author = "John Quinn and Ram Achuthanandam and Bugelski, {Peter J.} and Capocasale, {Renold J.} and Fisher, {Paul W.} and Moshe Kam and Leonid Hrebien",

year = "2005",

language = "English (US)",

pages = "34--35",

journal = "Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC",

issn = "1071-121X",

note = "Proceedings of the 2005 IEEE 31st Annual Northeast Bioengineering Conference ; Conference date: 02-04-2005 Through 03-04-2005",

}

TY - JOUR

T1 - A neural network approach to determining cellular viability

AU - Quinn, John

AU - Achuthanandam, Ram

AU - Bugelski, Peter J.

AU - Capocasale, Renold J.

AU - Fisher, Paul W.

AU - Kam, Moshe

AU - Hrebien, Leonid

PY - 2005

Y1 - 2005

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=28444441901&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=28444441901&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:28444441901

SN - 1071-121X

SP - 34

EP - 35

JO - Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC

JF - Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC

M1 - 2.1.1

T2 - Proceedings of the 2005 IEEE 31st Annual Northeast Bioengineering Conference

Y2 - 2 April 2005 through 3 April 2005

ER -

A neural network approach to determining cellular viability

Abstract

All Science Journal Classification (ASJC) codes

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