Abstract
While dealing with high-dimensional biological data, subsets of genes that have similar behaviour under some conditions but behave independently under others, are frequently found. Discovering such co-expressions can aid in uncovering genomic knowledge such as gene networks or gene interactions. A great deal of research is being carried out on the algorithms of these approaches for decades, especially for array mining in gene expression analysis in the field of computational biomedical. In this paper, we compare the results of K-means, traditional clustering algorithm and OPSM (Order-preserving submatrix problem), a biclustering algorithm on a gene expression dataset on the basis of different parameters and examine the benefits of co-clustering over traditional clustering methods in different applications.
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Mehta, D., Sehgal, S., Choudhury, T., Sarkar, T. (2021). A Comparative Analysis of Clustering and Biclustering Algorithms in Gene Analysis. In: Singh, J., Kumar, S., Choudhury, U. (eds) Innovations in Cyber Physical Systems. Lecture Notes in Electrical Engineering, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-16-4149-7_4
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DOI: https://doi.org/10.1007/978-981-16-4149-7_4
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