Abstract
In this work, we address the biclustering of gene expression data with evolutionary computation, which has been proven to have excellent performance on complex problems. In expression data analysis, the most important goal may not be finding the maximum bicluster, as it might be more interesting to find a set of genes showing similar behavior under a set of conditions. Our approach is based on evolutionary algorithms and searches for biclusters following a sequential covering strategy. In addition, we pay special attention to the fact of looking for high quality biclusters with large variation. The quality of biclusters found by our approach is discussed by means of the analysis of yeast and colon cancer datasets.
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References
Ben-Dor, A., Shamir, R., Yakhini, Z.: Clustering gene expression patterns. Journal of Computational Biology 6(3–4), 281–297 (1999)
Tanay, A., Sharan, R., Shamir, R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 19(sup. 2), 196–205 (2002)
Alon, U., Barkai, N., Notterman, D.A., et al.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. National Academy of Sciences of the United States of America 96, 6745–6750 (1999)
Mirkin, B.: Mathematical classification and Clustering. Kluwer, Dordrecht (1996)
Bleuler, S., Prelić, A., Zitzler, E.: An EA framework for biclustering of gene expression data. In: Congress on Evolutionary Computation (CEC 2004), Piscataway, NJ, pp. 166–173. IEEE, Los Alamitos (2004)
Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proceedings of the 8th International Conference on Itelligent Systems for Molecular Biology (ISMB 2000), pp. 93–103 (2000)
Cho, R., Campbell, M., Winzeler, E., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T., Gabrielian, A., Landsman, D., Lockhart, D., Davis, R.: A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2, 65–73 (1998)
Getz, G., Levine, E., Domany, E.: Coupled two–way clustering analysis of gene microarray data. In: Proceedings of the Natural Academy of Sciences, USA, pp. 12079–12084 (2000)
Hartigan, J.A.: Direct clustering of a data matrix. Journal of the American Statistical Association 67(337), 123–129 (1972)
Lazzeroni, L., Owen, A.: Plaid models for gene expression data. Technical report, Stanford University (2000)
Wang, H., Wang, W., Yang, J., Yu, P.S.: Clustering by pattern similarity in large data sets. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 394–405 (2002)
Yang, J., Wang, W., Wang, H., Yu, P.S.: δ–clusters: Capturing subspace correlation in a large data set. In: Proceedings of the 18th IEEE Conference on Data Engineering, pp. 517–528 (2002)
Yang, J., Wang, W., Wang, H., Yu, P.S.: Enhanced biclustering on expression data. In: Proceedings of the 3rd IEEE Conference on Bioinformatics and Bioengineering, pp. 321–327 (2003)
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Aguilar–Ruiz, J.S., Divina, F. (2005). Evolutionary Biclustering of Microarray Data. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_1
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DOI: https://doi.org/10.1007/978-3-540-32003-6_1
Publisher Name: Springer, Berlin, Heidelberg
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