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Biclustering on expression data: A review
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Microarrays are one of the latest breakthroughs in experimental molecular biology, which provide a powerful tool by which the expression patterns of thousands of genes can be monitored simultaneously and are already producing huge amount of valuable data. The concept of bicluster was introduced by Cheng and Church (2000) to capture the coherence of a subset of genes and a subset of conditions. ...
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An efficient node-deletion algorithm is introduced to find submatrices in expression data that have low mean squared residue scores and it is shown to perform well in finding co-regulation patterns in yeast and human. This introduces "biclustering", or simultaneous clustering of both genes and conditions, to knowledge discovery from expression data. This approach overcomes some problems associa...
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Biclustering is an important problem that arises in diverse applications, including the analysis of gene expression and drug interaction data. A large number of clustering approaches have been proposed for gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the exi...
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1.1 INTRODUCTION Microarrays allow measuring the expression level of a large number of genes under different experimental samples or environmental conditions. The data generated from them are called gene expression data. The extraction of biological relevant knowledge from this data is not a trivial task. Gene expression data are usually represented by a matrix M (see Table 1.1), where the i th...
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ژورنال
عنوان ژورنال: Journal of Biomedical Informatics
سال: 2015
ISSN: 1532-0464
DOI: 10.1016/j.jbi.2015.06.028