Sentence examples for clusters from multiple from inspiring English sources

The creation of spatially contiguous clusters from multiple sources of information is an important objective in many scientific disciplines.

Scientists have created mice with four or more parents by taking the developing cell clusters from multiple fertilized eggs and combining them into a single cell cluster which then develops into a single individual.

Figure 3 b shows that most of the individual clustering algorithms (used with the PPI network) give lower precision or recall compared to PPI+COMBINE, showing the utility of combining the clusters from multiple clustering algorithms.

Figure 2 b shows that individual clustering algorithms (used with the PPI network) give lower precision and recall compared to PPI+COMBINE, showing the utility of combining the clusters from multiple clustering algorithms.

Note that the original PPI decomposition approach (as described in [ 10]) did not aggregate clusters from multiple clustering algorithms, but used individual clustering algorithms instead; here we incorporate the COMBINE strategy for DECOMP as it was found to improve complex-discovery performance in [ 9].

Finally, Lpg seems to be the most distantly related, sharing 2,207 genes with Lpl (75%) and 1,792 (60.1%) with Lpp. Figure 2 shows the application of the rarefaction methodology on the gene clusters from multiple genomes belonging to the same species.

For such analyses, some advanced clustering approaches have been suggested, for example the utility of transcriptional consensus clusters derived from multiple cluster algorithms [8], or incorporation of prior knowledge of gene function [9].

Biosynthetic pathways can reach 100 kb in size; however, because cosmid and fosmid library inserts are restricted to around 40 kb, large pathways cannot be captured in their entirety as a single clone and further manipulations are frequently necessary to assemble a complete gene cluster from multiple cosmids.

The consistent storage of clustering results from multiple different clustering methods in a single database enables users to easily compare the effects of the choice of clustering algorithm and the choices of organisms to cluster on the predicted protein families.

Instead the metal achieves coordination completion by aggregation processes where clusters form from multiple combinations of ligands and metal ions, using μ2Ohydrazone, and μ2N2 diazine groups as potential bridges.

Two algorithms exhibit the best overall balance, with both sensitivity and specificity>80%: INPARANOID identifies orthologs across two species while OrthoMCL clusters orthologs from multiple species.