We assigned functions to each protein family by using multiple databases: the Clusters of Orthologous Groups (COG) [ 39, 40], JCVI [ 41], KEGG [ 42], SEED [ 43], Virulence Factors Database (VFDB) [ 44], MvirDB [ 45], Pfam [ 46], and Gene Ontology (GO) [ 47] database.
We performed risk factor analysis by using multiple logistic regression for cluster surveys, adjusting for clustering within farms.
For instance, the clustering result is extremely sensitive to the initial means, which can be mitigated by using multiple sets of initial means [65].
By using multiple narrators, McBurney allows for every possibility.
In the archive description scheme, the content of an archive is represented using multiple hierarchies of clusters, which may be related by entity-relation graphs.
All analyses will account for clustering (by store for individual-level outcomes and over time for store-level outcomes) using multiple regressions with clustered robust standard errors (e.g. Huber-White), generalized estimating equations (GEE), or multilevel modeling methods [ 45].
We demonstrate that using multiple parameters in cluster formation reduces energy consumption.
We used the tensor clustering framework to cluster MTBC strains using multiple biomarkers, and compared the clustering to SpolDB4 sublineages.
Here, we take advantage of the availability of sequenced fungal genomes and present a novel and unbiased method for isolating genes strongly associated with pathogenic species using comparative genomics analysis followed by an unsupervised clustering step using multiple protein properties.
Clustering analysis was done using multiple dimensional scaling and hierarchical clustering methods.
