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Each cluster was assigned to one of 18 predefined strata, based on predicted growing stock volume, time consumption for cluster measurements and the slope of the terrain [31].
The most characteristic GO term for each cluster was Assigned using FatiGO [31].
Each cluster was assigned a behaviour based on the visual identification.
This resulted in a total of 16,835 gene clusters, with cluster sizes ranging from 1 to 19 genes; each cluster was assigned a unique identifier (bID_Cluster_number; see Additional file 3: Table S2).
Each cluster was assigned a putative function and placed in a functional class based on the sequence homology to molecules identified by the BLAST results from the NCBI non-redundant protein, the Gene Ontology, the conserved domain, rRNA and mitochondrial databases.
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Annotation and assignment of functional categories for the genes in each cluster were assigned based on conserved PFAM domain predictions (http://pfam.sanger.ac.uk/) and gene ontology (GO) analysis (http://www.geneontology.org/).org/
Once the clusters are produced, each cluster is assigned its respective Unicode.
Each cluster is assigned a symbol, and the original signal is replaced by the corresponding sequence of symbols.
Once the primary and secondary ligatures are grouped into clusters and each cluster is assigned the respective Unicode, we proceed to training models to learn the ligature classes.
Each cluster is assigned a normalized amplitude A i that is computed from the tabulated cluster powers P i as A i = P i ∑ i = 1 I P i (49). in order to achieve a power normalization.
To evaluate the cluster purity, each cluster is assigned to the class which is most frequent in the cluster, and then the accuracy of this assignment is measured by counting the number of correctly assigned samples and dividing by n the number of data in the cluster.
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CEO of Professional Science Editing for Scientists @ prosciediting.com