Abstract
With the advent of the ”Age of Genomics”, generation, accumulation and analysis of gene expression datasets that contain expression levels of thousands of genes across different experimental conditions is emerging. Analysis of gene expression data is used in many areas including drug discovery and clinical applications. This proposed biclustering algorithm extracts maximum similarity bicluster using multiple node deletion method after applying feature selection. Experimental results show the effectiveness of the proposed algorithm.
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Bagyamani, J., Thangavel, K. (2010). SIMBIC: SIMilarity Based BIClustering of Expression Data. In: Das, V.V., et al. Information Processing and Management. BAIP 2010. Communications in Computer and Information Science, vol 70. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12214-9_73
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DOI: https://doi.org/10.1007/978-3-642-12214-9_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12213-2
Online ISBN: 978-3-642-12214-9
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