Biomedical Engineering Reference
In-Depth Information
References
1. W. Dubitzky, M. Granzow, and D. Berrar, Data Mining and Machine Learn-
ing Methods for Microarray Analysis, In: Methods of Microarray Data Anal-
ysis - Papers from CAMDA 2000, (S.M. Lin, K.F. Johnson, Eds.), pp. 5-22,
Kluwer Academic Publishers, 2001.
2. M.P.S. Brown, W.N. Grundy, D. Lin, N. Cristianini, C. Sugnet, T.S. Furey,
M. Ares, Jr., and D. Haussler, \Knowledge-based analysis of microarray gene
expression data using support vector machines." Proceedings of the National
Academy of Science, 97 (2000), 262-267.
3. A. Narayanan, A. Cheung, J. Gamalielsson, E. Keedwell, and C. Vercellone,
Articial Neural Networks for Reducing the Dimensionality of Gene Expres-
sion Data in Bioinformatics using Computational Intelligence, Seiert. U.,
2004.
4. J. Herrero, A. Valencia, and J. Dopazo, A hierarchical unsupervised growing
neural network for clustering gene expression patterns, Bioinformatics, 17
(2001), 126-136.
5. S.R. Holbrook and S.M. Muskal, Predicting Protein Structural Features With
Articial Neural Networks, Articial Intelligence and Molecular Biology, (L.
Hunter, ed.), AAAI Press, 1993.
6. M.C. O'Neill and L. Song, Neural network analysis of lymphoma microarray
data: prognosis and diagnosis near perfect, BMC Bioinformatics, 4 (2003),
4:13.
7. P. Tomsich, A. Rauber, and D. Merkl, Optimizing the parSOM Neural Net-
work Implementation for Data Mining with Distributed Memory Systems
and Cluster Computing, DEXA Workshop (2000), 661-668.
8. Y. Xu, D. Xu, and V. Olman, A Practical Method for Interpretation of
Threading Scores: An Application of Neural Network, Statistica Sinica 12
(2002), 159-177.
9. F. Rossia, N. Delannay, B. Conan-Gueza, and M. Verleysen, Representation
of functional data in neural networks, Neurocomputing, 64 (2005), 183-210.
10. J.J.B. Jack, D. Noble, and R.W. Tsien, Electric current ow in excitable
cells, Clarendon Press, Oxford, 1975.
11. H.C. Tuckwell, Introduction to theoretical neurobiology: volume 1 linear ca-
ble theory and dendritic structure, Cambridge University Press, New York,
1988.
12. N.K. Bose and P. Liang, Neural Network Fundamentals with Graphs, Algo-
rithms, and Applications, McGraw-Hill, New York, 1996.
13. G. Cybenko, Approximation by Superpositions of a sigmoidal function, Math-
ematics of Control, Signals, and Systems, 2(4) (1989), 303-314.
14. A. Engelbrecht, L. Fletcher and I. Cloete, Variance Analysis of Sensitivity
Information for Pruning Feedforward Neural Networks, IEEE International
Joint Conference on Neural Networks, Washington DC, USA, paper 379,
1999. 13.
Search WWH ::
Custom Search