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Assessing the quality of biclusters using fuzzy biclustering index
International Journal of Data Mining and Bioinformatics, 2016Several algorithms are proposed in the literature for extracting local patterns from a large data matrix. This technique of data mining is known as biclustering. Each of the biclustering algorithms is specialised in extracting different kinds of biclusters. Some algorithms detect equal biclusters, whereas some identify scaled biclusters Madeira et al.,
Nishchal K. Verma +2 more
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Pattern Recognition, 2017
Abstract Biclustering refers to the problem of simultaneously clustering the rows and columns of a given data matrix, with the goal of obtaining submatrices where the selected rows present a coherent behaviour in the selected columns, and vice-versa. To face this intrinsically difficult problem, we propose a novel generative model, where biclustering
M A T Figueiredo
exaly +4 more sources
Abstract Biclustering refers to the problem of simultaneously clustering the rows and columns of a given data matrix, with the goal of obtaining submatrices where the selected rows present a coherent behaviour in the selected columns, and vice-versa. To face this intrinsically difficult problem, we propose a novel generative model, where biclustering
M A T Figueiredo
exaly +4 more sources
Biclustering of Biological Sequences
2017 28th International Workshop on Database and Expert Systems Applications (DEXA), 2017The analysis of biological data is a challenging problem in bioinformatics and data mining field. Given the complexity of the analysis of biological information, several methods have been proposed for analyzing this biological information in databases mostly in the form of genetic sequences and protein structures.
Faouzi Mhamdi, Sourour Marai
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Biclustering with a quantum annealer
Soft Computing, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bottarelli, Lorenzo +5 more
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GFBA: A Biclustering Algorithm for Discovering Value-Coherent Biclusters
2007Clustering has been one of the most popular approaches used in gene expression data analysis. A clustering method is typically used to partition genes according to their similarity of expression under different conditions. However, it is often the case that some genes behave similarly only on a subset of conditions and their behavior is uncorrelated ...
Xubo Fei +3 more
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Biclustering of High-throughput Gene Expression Data with Bicluster Miner
2012 IEEE 12th International Conference on Data Mining Workshops, 2012During recent years, many biclustering algorithms have been developed for the analysis of gene expression data to complement and expand the capabilities of traditional clustering methods. With biclustering, genes with similar expression profiles can be identified not only over the whole data set but also across subsets of experimental conditions ...
András Király +3 more
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BAYESIAN BICLUSTERING FOR PATIENT STRATIFICATION
Biocomputing 2016, 2015The move from Empirical Medicine towards Personalized Medicine has attracted attention to Stratified Medicine (SM). Some methods are provided in the literature for patient stratification, which is the central task of SM, however, there are still significant open issues.
Sahand Khakabimamaghani, Martin Ester
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2018
Biclustering, which can be defined as the simultaneous clustering of rows and columns in a data matrix, has received increasing attention in recent years, being applied in many scientific scenarios (e.g. bioinformatics, text analysis, computer vision).
M. Denitto +3 more
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Biclustering, which can be defined as the simultaneous clustering of rows and columns in a data matrix, has received increasing attention in recent years, being applied in many scientific scenarios (e.g. bioinformatics, text analysis, computer vision).
M. Denitto +3 more
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2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006
Uncovering genetic pathways is equivalent to finding clusters of genes with expression levels that evolve coherently under subsets of conditions. This can be done by applying a biclustering procedure to gene expression data. Given a microarray data set with M genes and N conditions, we define a bicluster with coherent evolution as a subset of genes ...
Ahmed H. Tewfik +2 more
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Uncovering genetic pathways is equivalent to finding clusters of genes with expression levels that evolve coherently under subsets of conditions. This can be done by applying a biclustering procedure to gene expression data. Given a microarray data set with M genes and N conditions, we define a bicluster with coherent evolution as a subset of genes ...
Ahmed H. Tewfik +2 more
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A Combinatoric biclustering algorithm
2011The unsupervised analysis of gene expression data plays a very important role in Genetics experiments. That is why a lot of clustering and biclustering techniques have been proposed. Our choice of biclustering methods is motivated by the accuracy in the obtained results and the possibility to find not only rows or columns that provide a partition of ...
Ekaterina Nosova +2 more
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