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In this paper, we propose an enhanced framework for performing pClustering on actual gene expression analysis. Our new framework includes an effective data ...
Nov 30, 2016 · This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge
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Dec 12, 2023 · Gene clustering has been widely used to classify DEGs with similar expression patterns, but rarely used to identify DEGs themselves. We recently ...
Oct 3, 2023 · In this study, we established a novel computational framework, scFseCluster, for scRNA-seq clustering analysis. scFseCluster incorporates a ...
The K-Means algorithm is effective in producing clusters for many practical applications. But the computational complexity of the original K-Means algorithm is ...
Aug 25, 2007 · Clustering is an important tool for analyzing such microarray data, typical properties of which are its inherent uncertainty, noise and ...
This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge.
Microarrays allow researchers to perform simultaneous monitoring of the profiles of many gene expressions under diverse experimental environments.
Oct 25, 2018 · We present clust, a method that solves these problems by extracting clusters matching the biological expectations of co-expressed genes and outperforms widely ...