Evolutionary Algorithm Based on New Crossover for the Biclustering of Gene Expression Data

  Microarray represents a recent multidisciplinary technology. It measures the expression levels of several genes under different biological conditions, which allows to generate multiple data. These data can be analyzed through biclustering method to determinate groups of genes presenting a similar behavior under specific groups of conditions. This paper proposes a new evolutionary algorithm based on a new crossover method, dedicated to the biclustering of gene expression data. This proposed crossover method ensures the creation of new biclusters with better quality. To evaluate its performance, an experimental study was done on real microarray datasets. These experimentations show that our algorithm extracts high quality biclusters with highly correlated genes that are particularly involved in specific ontology structure

A review on probabilistic graphical models in evolutionary computation

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms

Applying Biclustering To Perform Collaborative Filtering

Collaborative filtering (CF) is a method to perform automated suggestions for a user based on the opinion of other users with similar interest. Most of the CF algorithms do not take into account the existent duality between users and items, considering only the similarities between users or only the similarities between items. In this paper we propose a novel methodology for the CF capable of dealing with this situation. By proposing an immune-inspired biclustering technique to carry out clustering of rows and columns at the same time, our algorithm is able to group similarities between users and items. In order to evaluate the proposed methodology, we have applied it to MovieLens dataset which contains user's ratings to a large set of movies. The results indicate that our proposal is able to provide useful recommendations for the users, outperforming other methodologies for CF reported in the literature. © 2007 IEEE.421426Agrawal, R., Gehrke, J., Gunopulus, D., Raghavan, P., Automatic subspace clustering of high dimensional data for data mining applications (1998) Proc. of the ACM/SIGMOD Int. Conference on Management of Data, pp. 94-105Cheng, Y., Church, G.M., Biclustering of expression data (2000) Proc. of the 8th Int. Conf. on Int. Systems for Molecular Biology, pp. 93-103de Castro, L.N., Von Zuben, F.J., (2001) aiNet: An Artificial Immune Network for Data Analysis, pp. 231-259. , Data Mining: A Heuristic Approachde França, F.O., Bezerra, G., Von Zuben, F.J., New Perspectives for the Biclustering Problem (2006) IEEE Congress on Evolutionary Computation, pp. 2768-2775Dhillon, I.S., Co-clustering documents and words using bipartite spectral graph partitioning (2001) Proc. of the 7th Int. Conf. on Knowledge Discovery and Data Mining, pp. 269-274Goldberg, D., Nichols, D., Brian, M., Terry, D., Using collaborative filtering to weave an information tapestry (1992) ACM Communications, 35, pp. 61-70Gomes, L.C.T., de Sousa, J.S., Bezerra, G.B., de Castro, L.N., Von Zuben, F.J., (2003) Copt-aiNet and the Gene Ordering Problem, 3 (2), pp. 27-33. , Information Technology MagazineHaixun, W., Wei, W., Jiong, Y., Yu, P.S., Clustering by pattern similarity in large data sets (2002) Proc. of the 2002 ACM SIGMOD Int. Conference on Management of Data, pp. 394-405Hartigan, J. A, Direct clustering of a data matrix. Journal of the American Statistical Association (JASA), 1972, 67, no. 337, pp. 123-129Moscato, P., Berretta, R., Mendes, A., A New Memetic Algorithm for Ordering Datasets: Applications in Microarray Analysis (2005) Proc. of the 6th Metaheuristics Int. Conference, pp. 695-700. , Austria, AugustResnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J., Grouplens: An open architecture for collaborative filtering on netnews (1994) In Proc. of the Computer Supported Collaborative Work Conference, pp. 175-186Segal, E., Taskar, B., Gasch, A., Friedman, N., Koller, D., Rich probabilistic models for gene expression (2001) In Bioinformatics, 17 (SUPPL. 1), pp. S243-S252Sheng, Q., Moreau, Y., De Moor, B., Biclustering micrarray data by Gibbs sampling (2003) Bioinformatics, 19 (SUPPL. 2), pp. 196-205Symeonidis, P., Nanopoulos, A., Papadopoulos, A., Manolopoulos, Y., Nearest-Biclusters Collaborative Filtering (2006) Proc. of the WebKDD - Workshop held in conjuction with KDDTang, C., Zhang, L., Zhang, I., Ramanathan, M., Interrelated two-way clustering: An unsupervised approach for gene expression data analysis (2001) Proc. of the 2nd IEEE Int. Symposium on Bioinformatics and Bioengineering, pp. 41-48Yu, K., Schwaighofer, A., Tresp, V., Xu, X., Kriegel, H.-P., Probabilistic Memory-based Collaborative Filtering (2004) In IEEE Transactions on Knowledge and Data Engineering, pp. 56-5