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The risk estimates for each cluster were identified.
Candidate motifs from each cluster were identified by CisModule or BioProspector.
A total of 39 clusters and one person for each cluster were identified.
The modifications driving each cluster were identified by determining those modifications with small binomial P-values across all of the sites in the cluster.
Because each cluster may contain genes involved in a common molecular pathway, over-represented KEGG pathways for each cluster were identified using the log-odds ratio (see Methods).
and pulsotypes within each cluster were identified with lower-case letters (C1a, C1b etc).. Somatic cell count, recurring or new cases of VTCM, daily milk yield, and culling due to mastitis were used as outcome measurements.
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Each cluster is identified as a data-driven DA.
Each cluster is identified by well-defined values of torsion angles and distances between the pharmacophoric groups.
Based on the clustering results, 33 TCL groups are formed, where each one consists of maximum 10 TCLs. Figure 5(c) shows the clustering of the 300 TCLs, where each cluster is identified by a particular color.
The probabilistic weight connecting two nodes in a graph is utilized to cluster the nodes in the graph using application of Steps 1 to 4. Only sub-goal states of each cluster are identified using application of method described in an earlier section on our approach to clustering of nodes of this work.
Step 3: The probabilistic weight connecting two nodes in a graph is utilized to cluster the nodes in the graph using application of Steps 1 to 4. Step 4: Only sub-goal states of each cluster are identified using application of method described in an earlier section on our approach to clustering of nodes of this work.
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