Abstract
In this work, we have extended the experimental analysis about an encoding approach for evolutionary-based algorithms proposed in [1], called probabilistic encoding. The potential of this encoding for complex problems is huge, as candidate solutions represent regions, instead of points, of the search space. We have tested in the context of gene expression biclustering problem, in a selection of a well-known expression matrix datasets. The results obtained for the experimental analysis reveals a satisfactory performance in comparison with other evolutionary-based algorithms, and a high exploration power in very large search spaces.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
In [1], IEPR was named as MOBPEOC (Multi-Objective Biclustering with Probabilistic Encoding and Overlapping Control).
References
Marcozzi, M., Divina, F., Aguilar-Ruiz, J.S., Vanhoof, W.: A novel probabilistic encoding for EAs applied to biclustering of microarray data. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 339–346. ACM, New York (2011)
Aguilar-Ruiz, J.S.: Shifting and scaling patterns from gene expression data. Bioinformatics 21, 3840–3845 (2005)
Berrar, D.P., Dubitzky, W., Granzow, M.: A Practical Approach to Microarray Data Analysis. Springer Publishing Company, Incorporated, US (2003)
Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE Trans. Comput. Biol. Bioinform. 1, 24–25 (2004)
Divina, F., Aguilar-Ruiz, J.S.: Biclustering of expression data with evolutionary computation. IEEE Trans. Knowl. Data Eng. 18(5), 590–602 (2006)
Mitra, S., Banka, H.: Multi-objective evolutionary biclustering of gene expression data. Pattern Recogn. 39(12), 2464–2477 (2006)
Pontes, B., Giráldez, R., Aguilar-Ruiz, J.: Configurable pattern-based evolutionary biclustering of gene expression data. Algorithms Mol. Biol. 8(1), 1–22 (2013)
Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pp. 93–103. AAAI Press (2000)
Yang, J., Wang, H., Wang, W., Yu, P.: Enhanced biclustering on expression data. In: Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering, BIBE 2003, p. 321. IEEE Computer Society, Washington, DC, USA (2003)
Pontes, B., Divina, F., Giráldez, R., Aguilar-Ruiz, J.S.: Improved biclustering on expression data through overlapping control. Int. J. Intell. Comput. Cybern. 3(2), 293–309 (2010)
Cruz, C., González, J.R., Pelta, D.A.: Optimization in dynamic environments: a survey on problems, methods and measures. Soft Comput. 15(7), 1427–1448 (2011)
Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched pareto genetic algorithm for multiobjective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence, vol. 1, pp. 82–87. IEEE (1994)
Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Divina, F., Pontes, B., Giraldez, R., Aguilar-Ruiz, J.S.: An effective measure for assessing the quality of biclusters. Comput. Biol. Med. 42(2), 245–256 (2012)
Acknowledgements
This research has been supported by the Spanish Ministry of Economy and Competitiveness under grants TIN2011-28956 and TIN2014-55894-C2-R.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Gil-Cumbreras, F.J., Giráldez, R., Aguilar-Ruiz, J.S. (2016). Extending Probabilistic Encoding for Discovering Biclusters in Gene Expression Data. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_59
Download citation
DOI: https://doi.org/10.1007/978-3-319-32034-2_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32033-5
Online ISBN: 978-3-319-32034-2
eBook Packages: Computer ScienceComputer Science (R0)