Modelling and clustering of gene expressions using RBFs and a shape similarity metric

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3177, Issue , 2004, Pages 1-10

  Möller Levet, Carla S ^a   Yin, Hujun ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 35048854253     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-28651-6_1     Document Type: Article

References (16)

1. Brown, P., Botstein, D.: Exploring the new world of the genome with DNA microarrays. Nature Genetics supplement 21 (1999) 33-37
2. Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., Futcher, B.: Comprehensive identification of cell cycle-regulated genes of yeast Saccharamyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9 (1998) 3273-3297
3. Dembélé, D., Kastner, P.: Fuzzy C-means method for clustering micoarray data. Bioinformatics 19 (2003) 973-980
4. Filkov, V., Skiena, S., Zhi, J.: Analysis techniques for microarray time-series data. Journal of Computational Biology 9 (2002) 317-330
5. Möller-Levet, C.S., Cho, K.H., Wolkenhauer, O.: Microarray data clustering based on temporal variation: FCV with TSD preclustering. Applied Bioinformatics 2 (2003) 35-45
6. Park, J., Sandberg, I.: Approximation and radial basis function networks. Neural Computing 5 (1993) 305-316
7. Bar-Joseph, Z., Gerber, G., Gifford, D.K., Jaakkola, T.S., Simon, I.: A new approach to analyzing gene expression time series data. In: Proceedings of RECOMB,. Washington DC, USA (2002) 39-48
8. Luan, Y., Li, H.: Clustering of time-course gene expression data using a mixed-effects model with B-splines. Bioinformatics 19 (2003) 474-482
9. Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks 2 (1991) 302-309
10. Möller-Levet, C.S., Yin, H., Cho, K.H., Wolkenhauer, O.: Modelling gene expression time-series with radial basis function neural networks. In: Proceeding of the International Joint Conference on Neural Networks (IJCNN'2004). (to appear)
11. Bezdek, J.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
12. Golay, X., Kollias, S., Stoll, G., Meier, D., Valavanis, A., Boesiger, P.: A new correlation-based fuzzy logic clustering algorithm for fMRI. Magnetic Resounance Medicine 40 (1998) 249-260
13. Pakhira, M.K., Bandyopedhyay, S., Maulik, U.: Validity index for crisp and fuzzy clusters. Pattern recognition 37 (2003) 487-501
14. Yager, R.R., Filev, D.P.: Approximate Clustering Via the Mouintain Method. IEE Transactions on systems, man, and cybernetics 24 (1994) 1279-1284
15. Luan, H., Li, H.: Model-based methods for identifying periodically expressed genes based on time course microrray gene expression data. Bioinformatics 20 (2004) 332-339
16. Klevecz, R.R., Dowse, H.B.: Tuning in the transcriptome: basins of attraction in th yeast cell cycle. Cell Prolif 33 (2000) 209-218