Sentence examples for promising clusters from inspiring English sources

In either case, the DISE clustering places the fragment clusters with favorable LE on top of the list, thus, directing the focus of the review team to the most promising clusters.

Many clusters were detected in individual SaTScan runs, so selection criteria were used to identify the most promising clusters for each analysis that reduced the false positive rate.

Based on a previous evaluation study28 we chose four of the most promising cluster validity indices, and found that the benefits of cluster validity indices were mostly confined to clustering methods (Fig. 3).

The results are promising; cluster analysis is able to group the linkograph into meaningful clusters, while entropy measures the opportunities for idea development of a team and can also reflect the opportunistic contributions of individual participants.

Once several of the most promising cluster solutions were identified, they were tested for robustness by repeatedly randomizing the list of observations and rerunning the k-means analysis, a process that accounts for the sensitivity of the clustering to the initial order of the cases (Mooi and Sarstedt, 2011).

Respectively, 4 and 13 were composed entirely of known pre-miRNAs at a median distance of 3327 and 237.5 bp while 5 and 10 contained precursor candidates at a median distance of 12 424 and 22 820 bp (see Supplementary Materials for the list of most promising cluster candidates).

By analysing the slope of the cost function with decreasing numbers of clusters possible promising numbers of clusters can be identified by determining significant increases in merging costs.

From the instability analysis promising numbers of clusters are three, four, five, seven and nine.

However, even with the bioinformatic identification of promising biosynthetic gene clusters, the detection and isolation of the resultant natural products often proves to be difficult given that the products of most biosynthetic pathways are present in extremely low quantities (if present at all) during laboratory cultivation.

Experimental results on both synthetic and real-life datasets show that SCC achieves promising classification and clustering results at one time.

First, we show that the theoretical properties of both components can be combined in a sound manner, promising an effective clustering framework.