Using a neural network with flow cytometry histograms to recognize cell surface protein binding patterns.
Flow cytometric systems are being used increasingly in all branches of biological science including medicine. To develop analytic tools for identifying unknown molecules such as the antibodies that recognize different structure in the identical antigens, we explored use of a neural network in flow cytometry data comparison. Peak locations were extracted from flow cytometry histograms and we used the Marquardt backpropagation neural networks to recognize identical or similar binding patterns between antibodies and antigens based on the peak locations. The neural network showed 93.8% to 99.6% correct classification rates for identical or similar molecules. This suggests that the neural network technique can be useful in flow cytometry histogram data analysis.
Kim
Eun-Young
EY
Decision Systems Group, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.
Zeng
Qing
Q
Rawn
James
J
Wand
Matthew
M
Young
Alan J
AJ
Milford
Edgar
E
Mentzer
Steven J
SJ
Greenes
Robert A
RA
D000911:Antibodies, Monoclonal / Q000378:metabolism*; D000954:Antigens, Surface / Q000378:metabolism*; D005434:Flow Cytometry; D008214:Lymphocytes / Q000378:metabolism; D008565:Membrane Proteins / Q000378:metabolism; D016571:Neural Networks, Computer; D011485:Protein Binding
D000911:Antibodies, Monoclonal; D000954:Antigens, Surface; D008565:Membrane Proteins