Sentence examples for core clusters with from inspiring English sources

Classical graph-based agglomerative methods employ a variety of similarity measures between nodes to partition PPI networks, but they often result in a poor clustering arrangement that contains one or a few giant core clusters with many tiny ones [1].

Each transcriptome was assembled using the de novo transcriptome assembler trinity (release 2011-10-29) [ 76] on a 48 core cluster with 256 GB RAM.

Computational facilities include a 168-core cluster, with each node connected to a high-speed DDR IB network and a scalable file system.

One more SATé run for each data set was employed on an 8-core cluster with a 24 hour limitation as well (Table S2).

Using this rather strict cutoff resulted in a set of 1729 core clusters, i.e. clusters with at least one protein for each of the 53 genomes in the study.

Similar results were obtained when confining the analyses to just the core clusters, i.e., the clusters with the largest numbers of members, where there was 98% (2,122 of 2,266) agreement between the two methods.

Finally, the last step is responsible for the merge of core clusters and it is performed with a single MapReduce algorithm.

The analysis performed with 150 core clusters showed a total of 27 33 rearrangements required to explain the changes in the genomic architecture from the ancestral free-living relative (the most recent common ancestor with H. elongata and C. salexigens) to the three endosymbionts (fig. 8).

The top 25 genes that were differentially expressed in the sarcomatoid SCK cells compared to the three adenocarcinomatous CC lines are in Additional file 6. Clustering data within groups revealed that the core clusters I and II were associated with transdifferentiation. Genes in cluster I appeared to be downregulated in the SCK cells, compared to ordinary CC cells.

The hierarchical clustering agreed fully with the assignment of samples to core clusters.

The clusters C in ℬ with (C) > 0 are then partitioned into core clusters and ambiguous clusters (Lines 13-17).