Sentence examples for problematic clusters from inspiring English sources

Using high read counts to filter out problematic clusters is an intuitive approach to remove highly repetitive loci, but high stochastic variation in read counts among single copy loci makes it practical to remove only the most egregious and obvious artifacts with this type of filter.

This is particularly problematic for clustering algorithms but also has significant consequences for other dimensionality reduction techniques.

This goal requires accurate reconstruction of CDS portions of the bona fide transcripts expressed in the target tissues, which may be problematic with clustered EST as mentioned above.

For those data, we observed that the SNPs detected by the two-locus QC were obviously problematic in their cluster plots and GenTrain scores.

by non-overlapping concentration ellipses was possible only when either the spectra D_tei_2, D_tei_3 and D_fun_5, whose correct assignment had already proved to be problematic in the cluster analysis, were excluded from the PCA or the distinction criterion was relaxed to 90% probability.

As discussed below, the use of the Bayesian Information criterion (BIC) or the Akaike Information criterion for estimating clusters is problematic.

Although Carsonella pangenome contains 54 strain-specific genes (most of them hypothetical proteins), this number could be an artifact due to the problematic assignment to orthologous clusters of several Carsonella genes owing to its high evolutionary divergence.

An inherent difficulty is that the strategy of searching for dense clusters becomes problematic: fully dense (ie. cliques) size-2 and size-3 clusters correspond to edges and triangles respectively, and only a few among the abundant edges and triangles of the PPI network represent actual small complexes.

Full densification of AMCs with high particle concentration was problematic because of ceramic cluster formations in the microstructure.

Such edits are aimed at improving the problematic part of the clustering pointed out by the human-in-the-loop.

The assessment involved parallel testing in three key areas: (1) filtering of data to remove problematic mark-ups; (2) clustering methods of multiple notations per crater; and (3) derivation of alternative crater degradation indices, based on the statistical variability of multiple notations and the smoothness of local image structures.