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Identification of 72 phytoplankon species by radial basis function neural network analysis of flow cytometric data

Identification of 72 phytoplankon species by radial basis function neural network analysis of flow cytometric data
Identification of 72 phytoplankon species by radial basis function neural network analysis of flow cytometric data
Radial basis function artificial neural networks (ANNs) were trained to discriminate between phytoplankton species based on 7 flow cytometric parameters measured on axenic cultures. Comparison was made between the performance of networks restricted to using radially-symmetric basis functions and networks using more general arbitrarily oriented ellipso~dal basis functions, with the latter proving significantly superior in performance. ANNs trained on 62, 54 and 72 taxa identified them with respectively 77, 73 and 70% overall success. As well as high success in identification, high confidence of correct identification was also achieved. Misidentifications resulted from overlap of character distributions. Improved overall identification success can be achieved by grouping together species with similar character distributions. This can be done within genera or based on groupings indicated in dendrograms constructed for the data on all species. When an ANN trained on 1 data set was tested with data on cells grown under different light conditions, overall successful identification was low (<20%), but when an ANN was trained on a combined data set identification success was high (>?0%). Clearly it is essential to include data on cells covering the whole spectrum of biological variatlon. Ways of obtaining data for training ANNs to identify phytoplankton from field samples are discussed.
0171-8630
47-59
Boddy, L.
c96980fe-5e9c-457a-8759-c3692f5cb61c
Morris, C.W.
aca73c43-1f12-4ba9-94a9-017e3e152f84
Wilkins, M.F.
4ed1631d-0122-46ea-9422-a5a7c32b0693
Al-Haddad, L.
acf335e3-a128-47e9-aec1-45b1a0495588
Tarran, G.A.
c6e9fb51-321c-4fb6-a2b0-00a58c344d73
Jonker, R.R.
40d09d71-9b1d-4d24-b326-6870af0cad26
Burkill, P.H.
91175019-8b55-4fb5-84ea-334c12de2557
Boddy, L.
c96980fe-5e9c-457a-8759-c3692f5cb61c
Morris, C.W.
aca73c43-1f12-4ba9-94a9-017e3e152f84
Wilkins, M.F.
4ed1631d-0122-46ea-9422-a5a7c32b0693
Al-Haddad, L.
acf335e3-a128-47e9-aec1-45b1a0495588
Tarran, G.A.
c6e9fb51-321c-4fb6-a2b0-00a58c344d73
Jonker, R.R.
40d09d71-9b1d-4d24-b326-6870af0cad26
Burkill, P.H.
91175019-8b55-4fb5-84ea-334c12de2557

Boddy, L., Morris, C.W., Wilkins, M.F., Al-Haddad, L., Tarran, G.A., Jonker, R.R. and Burkill, P.H. (1970) Identification of 72 phytoplankon species by radial basis function neural network analysis of flow cytometric data. Marine Ecology Progress Series, 195, 47-59. (doi:10.3354/meps195047).

Record type: Article

Abstract

Radial basis function artificial neural networks (ANNs) were trained to discriminate between phytoplankton species based on 7 flow cytometric parameters measured on axenic cultures. Comparison was made between the performance of networks restricted to using radially-symmetric basis functions and networks using more general arbitrarily oriented ellipso~dal basis functions, with the latter proving significantly superior in performance. ANNs trained on 62, 54 and 72 taxa identified them with respectively 77, 73 and 70% overall success. As well as high success in identification, high confidence of correct identification was also achieved. Misidentifications resulted from overlap of character distributions. Improved overall identification success can be achieved by grouping together species with similar character distributions. This can be done within genera or based on groupings indicated in dendrograms constructed for the data on all species. When an ANN trained on 1 data set was tested with data on cells grown under different light conditions, overall successful identification was low (<20%), but when an ANN was trained on a combined data set identification success was high (>?0%). Clearly it is essential to include data on cells covering the whole spectrum of biological variatlon. Ways of obtaining data for training ANNs to identify phytoplankton from field samples are discussed.

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Published date: 1 January 1970

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Local EPrints ID: 58253
URI: http://eprints.soton.ac.uk/id/eprint/58253
ISSN: 0171-8630
PURE UUID: cb9b7743-8931-4c10-b880-8a067e8792c2

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Date deposited: 12 Aug 2008
Last modified: 13 Mar 2019 20:32

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Contributors

Author: L. Boddy
Author: C.W. Morris
Author: M.F. Wilkins
Author: L. Al-Haddad
Author: G.A. Tarran
Author: R.R. Jonker
Author: P.H. Burkill

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