Identification of phytoplankton from flow cytometry data by using radial basis function neural networks

Applied and Environmental Microbiology

Volumn 65, Issue 10, 1999, Pages 4404-4410

  Wilkins, M F ^a   Boddy, Lynne ^a   Morris, C W ^b   Jonker, R R ^c,d  

^a CARDIFF UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF GLAMORGAN   (United Kingdom)

^c UNIVERSITY OF AMSTERDAM   (Netherlands)

^d AquaSense Ltd   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; FLOW CYTOMETRY; IDENTIFICATION METHOD; PHYTOPLANKTON;

ARTICLE; ARTIFICIAL NEURAL NETWORK; FLOW CYTOMETRY; INSTRUMENT; NONHUMAN; PHYTOPLANKTON;

EID: 0032832628     PISSN: 00992240     EISSN: None     Source Type: Journal    
DOI: 10.1128/aem.65.10.4404-4410.1999     Document Type: Article

References (28)

1. Balfoort, H. W., J. Snoek, J. R. M. Smits, L. W. Breedveld, J. W. Hofstraat, and J. Ringelberg. 1992. Automatic identification of algae: neural network analysis of flow cytometric data. J. Plankton Res. 14:575-589.
2. Boddy, L., C. W. Morris, M. F. Wilkins, G. A. Tarran, and P. H. Burkill. 1994. Neural network analysis of flow cytometric data for five marine phytoplankton groups. Cytometry 15:283-293.
3. Boddy, L., and C. W. Morris. Artificial neural networks for pattern recognition. In A. Fielding (ed.), Machine learning methods for ecological applications. Kluver, London, United Kingdom, in press.
4. Carr, M. R., G. A. Tarran, and P. H. Burkill. 1996. Discrimination of marine phytoplankton species through the statistical analysis of their flow cytometric signatures. J. Plankton Res. 18:1225-1238.
5. Cunningham, A., and G. A. Buonaccorsi. 1992. Narrow angle forward light scattering from individual algal cells: implications for size and shape discrimination in flow cytometry. J. Plankton Res. 14:223-234.
6. Demers, S., J. Kim, P. Legendre, and L. Legendre. 1992. Analysing multivariate flow cytometric data in aquatic sciences. Cytometry 13:291-299.
7. Dubelaar, G. B. J., A. Cunningham, A. C. Groenewegen, J. Klijstra, R. R. Jonker, J. Ringelberg, J. C. H. Peeters, T. P. A. Rutten, G. A. Vriezekolk, J. Wietzorrek, V. Kachel, J. W. König, J. J. F. Van Veen, L. Boddy, M. F. Wilkins, C. W. Morris, M. R. Carr, G. Tarran, P. H. Burkill, and A. E. R. Reeker. 1995. A European Optical Plankton Analysis System: flow cytometer based technology for automated phytoplankton identification and quantification, p. 945-956. In M. Weydert, E. Lipiatou, R. Goni, C. Frangakis, M. Bohle-Carbonell, and K. G. Barthel (ed.), Marine science and Technologies 2nd MAST days and EUROMAR market. CEC, Brussels, Belgium.
8. Frankel, D. S., R. J. Olson, S. L. Frankel, and S. W. Chisholm. 1989. Use of a neural network computer system for analysis of flow cytometric data of phytoplankton populations. Cytometry 10:540-550.
9. Frankel, D. S., S. L. Frankel, B. J. Binder, and R. F. Vogt. 1996. Application of neural networks to flow cytometry data analysis and real-time cell classification. Cytometry 23:290-302.
10. Guillard, R. R. L. 1975. Culture of phytoplankton for feeding marine invertebrates, p. 29-60. In W. L. Smith and M. H. Chanley (ed.), Culture of marine invertebrate animals. Plenum Press, New York, N.Y.
11. Haykin, S. 1994. Neural networks: a comprehensive foundation. Maxwell MacMillan International, New York, N.Y.
12. Hofstraat, J. W., M. E. J. de Vreeze, W. J. M. van Zeijl, L. Peperzak, J. C. H. Peeters, and H. W. Balfoort. 1991. Flow cytometric discrimination of phytoplankton classes by fluorescence and exitation properties. J. Fluoresc. 1: 249-265.
13. Hush, D. R., and B. G. Horne. 1993. Progress in supervised neural networks - what's new since Lippmann? IEEE Sig. Proc. Mag. 10:8-39.
14. Jonker, R. R., J. T. Meulemans, G. B. J. Dubelaar, M. F. Wilkins, and J. Ringelberg. 1995. Flow cytometry: a powerful tool in analysis of biomass distributions in phytoplankton. Water Sci. Technol. 32:17-182.
15. Kohonen, T. 1988. An introduction to neural computing. Neural Networks 1: 3-16.
16. Kohonen, T. 1988. Self-organisation and associative memory, 2nd ed. Springer-Verlag, New York, N.Y.
17. Lee, S., and R. M. Kil. 1991. A gaussian potential function network with hierarchically self-organizing learning. Neural Networks 4:207-224.
18. Morgan, A., L. Boddy, C. W. Morris, and J. E. M. Mordue. 1998. Identification of species in the genus Pestalotiopsis from spore morphometric data: a comparison of some neural and non-neural methods. Mycol. Res. 102: 975-984.
19. Morris, C. W., and L. Boddy. 1996. Classification as unknown by RBF networks: discriminating phytoplankton taxa from flow cytometry data, p. 629-634. In C. H. Dagli, M. Akay, C. L. P. Chen, B. R. Fernandez, and J. Ghosh (ed.), Intelligent engineering systems through artificial neural networks, vol. 6. ASME Press, New York, N.Y.
20. Musavi, M. T., W. Ahmed, K. H. Chan, K. B. Faris, and D. M. Hummels. 1992. On the training of radial basis function classifiers. Neural Networks 5: 595-603.
21. Richard, M. D., and R. P. Lippmann. 1991. Neural network classifiers estimate Bayesian a posteriori probabilities. Neural Computation 3:461-483.
22. Smits, J. R. M., L. W. Breedveld, M. J. W. Derksen, G. Kateman, H. W. Balfoort, J. Snoek, and J. W. Hofstraat. 1992. Pattern classification with artificial neural networks: classification of algae, based upon flow cytometer data. Anal. Chim. Acta 258:11-25.
23. Tou, J. T., and R. C. Gonzalez. 1974. Pattern recognition principles. Addison-Wesley, London, United Kingdom.
24. van Llere, L., and L. R. Mur. 1978. Light limited cultures of the blue green alga Oscillatoria agardhii. Mii. Internat. Ver. Limnol. 21:158-167.
25. Wettschereck, D., and T. Dietterich. 1992. Improving the performance of radial basis function networks by learning center locations. Adv. Neural Info. Proc. Syst. 4:1133-1140.
26. Wietzorrek, J., M. Stadler, and V. Kachel. 1994. Video cytometric imaging implemented in the EurOPA flow cytometer - a novel method for identification of marine organisms, p. 689-695. In Proceedings of Oceans 94 OSATES. OSATES, Brest, France.
27. Wilkins, M. F., C. W. Morris, and L. Boddy. 1994. A comparison of radial basis function and backpropagation neural networks for identification of marine phytoplankton from multivariate flow cytometry data. CABIOS 10: 285-294.
28. Wilkins, M. F., L. Boddy, C. W. Morris, and R. R. Jonker. 1996. A comparison of some neural and non-neural methods for identification of phytoplankton from flow cytometry data. CABIOS 12:9-18.