Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Enhanced Biclustering on Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
d-Clusters: Capturing Subspace Correlation in a Large Data Set
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
The maximum edge biclique problem is NP-complete
Discrete Applied Mathematics
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Biclustering Models for Structured Microarray Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
Data Analysis and Visualization in Genomics and Proteomics
Data Analysis and Visualization in Genomics and Proteomics
Possibilistic approach for biclustering microarray data
Computers in Biology and Medicine
The equation for response to selection and its use for prediction
Evolutionary Computation
Robust approach for estimating probabilities in Naïve-Bayes Classifier for gene expression data
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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Background: Microarray technology allows to measure the expression of thousands of genes simultaneously, and under tens of specific conditions. Clustering and Biclustering are the main tools to analyze gene expression data obtained from microarray experiments. By grouping together genes with the same behavior across samples, relevant biological knowledge may be extracted. Non-exclusive groupings are required, since a gene may play more than one biological role. Gene Shaving [Hastie, T., et al. (2000). Gene Shaving as a method for identifying distinct sets of genes with similar expression. Genome Biology, 1, 1-21] is a popular clustering algorithm which looks for coherent clusters of genes with high variance across samples, allowing overlapping among the clusters. Method: In this paper, we present an intelligent system for analyzing microarray data. Our system implements three novel non-exclusive approaches for clustering and biclustering whose aim is to find coherent groups of genes with large between-sample variance: EDA-Clustering and EDA-Biclustering, based on Estimation of Distribution Algorithms (EDA), and Gene-&-Sample Shaving, a biclustering algorithm based on Principal Components Analysis. Results: We integrated the three proposed methods into a web-based platform and tested their performance on two real datasets. The obtained results outperform Gene Shaving in terms of quality and size of revealed patterns. Furthermore, our system allows to visualize the results and validate them from a biological point of view by means of the annotations of the Gene Ontology.