Abstract
The most widespread biclustering algorithms use the Mean Squared Residue (MSR) as measure for assessing the quality of biclusters. MSR can identify correctly shifting patterns, but fails at discovering biclusters presenting scaling patterns. Virtual Error (VE) is a measure which improves the performance of MSR in this sense, since it is effective at recognizing biclusters containing shifting patters or scaling patterns as quality biclusters. However, VE presents some drawbacks when the biclusters present both kind of patterns simultaneously. In this paper, we propose a improvement of VE that can be integrated in any heuristic to discover biclusters with shifting and scaling patterns simultaneously.
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References
Aguilar-Ruiz, J.S.: Shifting and scaling patterns from gene expression data. Bioinformatics 21, 3840–3845 (2005)
Aguilar-Ruiz, J.S., Rodriguez, D.S., Simovici, D.A.: Biclustering of gene expression data based on local nearness. In: Proceedings of EGC 2006, Lille, France, pp. 681–692 (2006)
Baldi, P.: DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling. Cambridge University Press, Cambridge (2002)
Bleuler, S., Prelić, A., Zitzler, E.: An EA framework for biclustering of gene expression data. In: Congress on Evolutionary Computation (CEC-2004), pp. 166–173. IEEE, Los Alamitos (2004)
Bryan, K., Cunningham, P., Bolshakova, N.: Application of simulated annealing to the biclustering of gene expression data. IEEE Transactions on Information Technology on Biomedicine (2006)
Cano, C., Adarve, L., López, J., Blanco, A.: Possibilistic approach for biclustering microarray data. Computers in Biology and Medicine 37(10), 1426–1436 (2007)
Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proceedings of the 8th International Conference on Intellingent Systemns for Molecular Biology, La Jolla, CA, pp. 93–103 (2000)
Cho, H., Dhillon, I.S.: Effect of data transformation on residue. Technical report (2007)
Coelho, G.P., de Franca, F.O., Zuben, F.J.V.: Multi-objective biclustering: When non-dominated solutions are not enough. Journal of Mathematical Modelling and Algorithms 8(2), 175–202 (2009)
Divina, F., Aguilar-Ruiz, J.S.: Biclustering of expression data with evolutionary computation. IEEE Transactions on Knowledge & Data Engineering 18(5), 590–602 (2006)
Divina, F., Aguilar-Ruiz, J.S., Pontes, B., Giráldez, R.: An effective measure for assessing the quality of biclusters (in Press, 2010)
Hartigan, J.: Direct clustering of a data matrix. Journal of the American Statistical Association 67(337), 123–129 (1972)
Liu, J., Li, Z., Hu, X., Chen, Y.: Biclustering of microarray data with mospo based on crowding distance. BMC bioinformatics 10(suppl. 4), S9+ (2009)
Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: A survey. IEEE Transactions on Computational Biology and Bioinformatics 1, 24–25 (2004)
Pontes, B., Divina, F., Giráldez, R., Aguilar-Ruiz, J.S.: Virtual error: A new measure for evolutionary biclustering. In: Fifth European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio 2007), pp. 217–222 (2007)
Pontes, B., Giráldez, R., Divina, F., Martínez-Álvarez, F.: Evaluación de biclusters en un entorno evolutivo. In: IV Taller nacional de minería de datos y aprendizaje (TAMIDA), pp. 1–10 (2007)
Tanay, A., Sharan, R., Shamir, R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 18, 136–144 (2002)
Tilstone, C.: Dna microarrays: Vital statistics. Nature 424, 610–612 (2003)
Wang, H., Wang, W., Yang., J., Yu, P.S.: Clustering by pattern similarity in large data sets. In: ACM SIGMOD International Conference on Management of Data, Madison, WI, pp. 394–405 (2002)
Xu, X., Lu, Y., Tung, A.K.H., Wang, W.: Mining shifting-and-scaling co-regulation patterns on gene expression profiles. In: 22nd International Conference on Data Engineering (ICDE’06), pp. 89–99 (2006)
Yang, J., Wang, H., Wang, W., Yu, P.S.: An improved biclustering method for analyzing gene expression profiles. International Journal on Artificial Intelligence Tools 14, 771–790 (2005)
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Pontes, B., Giráldez, R., Aguilar-Ruiz, J.S. (2010). Measuring the Quality of Shifting and Scaling Patterns in Biclusters. In: Dijkstra, T.M.H., Tsivtsivadze, E., Marchiori, E., Heskes, T. (eds) Pattern Recognition in Bioinformatics. PRIB 2010. Lecture Notes in Computer Science(), vol 6282. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16001-1_21
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DOI: https://doi.org/10.1007/978-3-642-16001-1_21
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