Identification of phytoplankton from flow cytometry data by using radial basis function neural networks.
We describe here the application of a type of artificial neural network, the Gaussian radial basis function (RBF) network, in the identification of a large number of phytoplankton strains from their 11-dimensional flow cytometric characteristics measured by the European Optical Plankton Analyser instrument. The effect of network parameters on optimization is examined. Optimized RBF networks recognized 34 species of marine and freshwater phytoplankton with 91. 5% success overall. The relative importance of each measured parameter in discriminating these data and the behavior of RBF networks in response to data from "novel" species (species not present in the training data) were analyzed.
Wilkins
M F
MF
Cardiff School of Biosciences, University of Cardiff, Cardiff CF1 3TL, United Kingdom.
Boddy
L
L
Morris
C W
CW
Jonker
R R
RR
D005434:Flow Cytometry; D016571:Neural Networks, Computer; D010839:Phytoplankton / Q000302:isolation & purification*