Sentence examples for classes of clusters from inspiring English sources
Further on, we continue the investigation of a model approach accounting for two different classes of clusters: dilute and dense.
These analyses revealed three classes of clusters based on gene content and nucleotide sequence identity.
This progenitor cluster has given rise to a small number of distinct phylogenetic classes of clusters that are represented in a discontinuous pattern throughout ascomycetes.
Secondly, we used two classes of clusters; the top and bottom peaks, for the downstream analyses to interpret properties of clusters with multiple scales.
This issue has important implications because two main classes of clusters have been described (Weber and Hurst 2011): small clusters of highly coexpressed genes (likely constrained by shared CREs) and large clusters of housekeeping and unrelated (i.e., nonhomologous) genes.
We also confirmed that the histone modifications H3K36me3 and H3K4me3, together with Pol II, could be used to separate clusters involved in transcription from clusters more related to long-range interactions, although the functions of these two classes of clusters may overlap somewhat.
However, certain classes of clustering and/or broadcasting algorithms might be suitable to implement our protocol.
This suggests that partial tests were conducted (corresponding JUnit test code) on some large classes of cluster 4. We analyzed the source code of the classes of cluster 4. We found that 5 of the 18 classes that contains cluster 4, which are large and containing many methods, have only one complex method for which JUnit test code has been developed.
Oversimplifying and without completeness, given the variety of approaches present in literature, one can distinguish two main classes of clustering approaches.
Two further classes of cluster were observed: ones where there were no intervening genes that were not members of the same co-expression group (LCs 7, 11, 13, 14, 15 and 17), and having an average size of five genes.
Then a factored HMM in which the hidden variable has values drawn from the Cartesian product = [ C1] × ··· × [ C m ], where [ C l ] is the set of equivalence classes of cluster C l generated by using the counting partition, satisfies Condition II.
