Abstract
In this paper a hybrid metaheuristic for biclustering based on Scatter Search and Genetic Algorithms is presented. A general scheme of Scatter Search has been used to obtain high–quality biclusters, but a way of generating the initial population and a method of combination based on Genetic Algorithms have been chosen. Experimental results from yeast cell cycle and human B-cell lymphoma are reported. Finally, the performance of the proposed hybrid algorithm is compared with a genetic algorithm recently published.
Keywords
- Biclustering
- Gene Expression Data
- Scatter Search
- Evolutionary Computation
Download conference paper PDF
References
Larranaga, P., et al.: Machine learning in bioinformatics. Briefings in Bioinformatics 7(1), 86–112 (2006)
Busygin, S., Prokopyev, O., Pardalos, P.M.: Biclustering in data mining. Computers and Operations Research 35(9), 2964–2987 (2008)
Levine, E., Getz, G., Domany, E.: Couple two-way clustering analysis of gene microarray data. Proceedings of the National Academy of Sciences (PNAS) of the USA 97(22), 12079–12084 (2000)
Cheng, Y., Church, G.M.: Biclustering of Expression Data. In: 8th International Conference on Intelligent Systems for Molecular Biology, pp. 93–103 (2000)
Tanay, A., Sharan, R., Shamir, R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 18(1), 136–144 (2002)
Yang, J., Wang, H., Wang, W., Yu, P.: Enhanced biclustering on expression data. In: 3rd IEEE Symposium on Bioinformatics and Bioengineering, pp. 321–327 (2003)
Bergmann, S., Ihmels, J., Barkai, N.: Iterative signature algorithm for the analysis of large-scale gene expression data. Physical Review E 67(3), 31902 (2003)
Harpaz, R., Haralick, R.: Exploiting the geometry of gene expression patterns for unsupervised learning. In: 18th International Conference on Pattern Recognition (ICPR 2006), pp. 670–674 (2006)
Bryan, K., Cunningham, P., Bolshakova, N., Coll, T., Dublin, I.: Biclustering of expression data using simulated annealing. In: 18th IEEE International Symposium on Computer-Based Medical Systems, pp. 383–388 (2005)
Divina, F., Aguilar-Ruiz, J.S.: Biclustering of Expression Data with Evolutionary Computation. IEEE Transactions on Knowledge and Data Engineering 18(5), 590–602 (2006)
Mitra, S., Banka, H.: Multi-objective evolutionary biclustering of gene expression data. Pattern Recognition 39(12), 2464–2477 (2006)
Aguilar-Ruiz, J.S.: Shifting and scaling patterns from gene expression data. Bioinformatics 21(20), 3840–3845 (2005)
Nepomuceno, J.A., Troncoso, A., Aguilar-Ruiz, J.S., Garcıa-Gutierrez, J.: Biclusters Evaluation Based on Shifting and Scaling Patterns. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.) IDEAL 2007. LNCS, vol. 4881, pp. 840–849. Springer, Heidelberg (2007)
Marti, R., Laguna, M.: Scatter Search. Methodology and Implementation in C. Kluwer Academic Publishers, Boston (2003)
Cho, R.J., et al.: A Genome-Wide Transcriptional Analysis of the Mitotic Cell Cycle. Molecular Cell 2(1), 65–73 (1998)
Alizadeh, A.A., et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nepomuceno, J.A., Troncoso, A., Aguilar–Ruiz, J.S. (2009). A Hybrid Metaheuristic for Biclustering Based on Scatter Search and Genetic Algorithms. In: Kadirkamanathan, V., Sanguinetti, G., Girolami, M., Niranjan, M., Noirel, J. (eds) Pattern Recognition in Bioinformatics. PRIB 2009. Lecture Notes in Computer Science(), vol 5780. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04031-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-04031-3_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04030-6
Online ISBN: 978-3-642-04031-3
eBook Packages: Computer ScienceComputer Science (R0)
-
Published in cooperation with
http://www.iapr.org/
