1996
DOI: 10.1103/physrevlett.76.3251
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Superparamagnetic Clustering of Data

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Cited by 480 publications
(521 citation statements)
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“…With the fraction of the sequences in the subalignment set to f =0.35 (see Methods) in the SCA, there are 74 allowed perturbations (S j = 0 for j = 1, 2, 3...74) at the various positions in the DHFR family. We used the clustering protocol [42,43] to identify the set of co-varying residues. After rescaling the ∆∆G ij matrix (Eq.…”
Section: Resultsmentioning
confidence: 99%
“…With the fraction of the sequences in the subalignment set to f =0.35 (see Methods) in the SCA, there are 74 allowed perturbations (S j = 0 for j = 1, 2, 3...74) at the various positions in the DHFR family. We used the clustering protocol [42,43] to identify the set of co-varying residues. After rescaling the ∆∆G ij matrix (Eq.…”
Section: Resultsmentioning
confidence: 99%
“…The super paramagnetic clustering (SPC) algorithm 16 was used for performing cluster analysis. In the resulting dendrogram (Figure 1), stable (i.e., statistically significant) gene clusters are identified; these are denoted G2-G12 (see Supplementary Information for the complete list of genes in each cluster).…”
Section: Clustering Analysismentioning
confidence: 99%
“…Clustering DNA chip data by means of the SPC algorithm (Blatt et al, 1996) is an unsupervised approach that aims at finding groups of genes with similar expression profiles (Kannan et al, 2001). Applying this method to the 573 genes identified in NHEK (Figure 4a) revealed a clear-cut partitioning/ distinction between up-and downregulated ones (Figure 5a).…”
Section: Cluster Analysismentioning
confidence: 99%