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In support of our approach, genes with known developmental expression profiles were mapped in expected clusters and used to guide the annotation of the expression map.
Cut-offs of 5, 10, 20, 40, 50, 100, 200 and 500 provide the user fined-tuned control of the number of expected clusters and the resulting size of each cluster.
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As expected, clusters C1 and C2 in the same subspecies had a closer relationship (0.284) than these clusters in different subspecies (0.452 and 0.406), indicating that var.
As expected, clusters B, C and D contain a large number of ESAGs that were up-regulated in both BF conditions.
We can exclude that clusters primarily form as a result of cross-hybridization, since (1) members of well-characterized gene families appear in different (the expected) clusters (eg smooth muscle and cardiac actins) and since (2) co-expressed genes tended to share GO terms or to encode interacting proteins to a higher degree than they tended to be paralogs (supplementary data, additional file 1).
Because the numbers of expected clusters were unknown, other clustering methods, including K-means clustering and self-organizing maps [reviewed in [ 21]], were not employed.
Hence, this set is used as input to k-means and a given number of expected clusters (i.e. k parameter).
As a result, the total number of expected clusters with multiple interactions is expected to be low at C- and H-levels.
First, the Laplacian matrix, L, is generated by PsimMat, and its eigenvalue distribution is used to determine the number of expected clusters [26].
First, the number of expected clusters can be determined by spectral clustering in a limited time.
In addition, the K expected clusters maybe not found by searching ( {C}_M^K ) times.
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CEO of Professional Science Editing for Scientists @ prosciediting.com