Sentence examples for challenge cluster from inspiring English sources

What is also clear is that all methods were very good for most of the compounds in the red challenge cluster (shown at the top, right-most cluster).

MS-FINDER outperformed other Category 2 approaches in the green challenge cluster (second from left)—showing the complementarity of the different approaches.

This is also shown well in the green challenge cluster, where the improvements that the metadata and machine learning add beyond the combinatorial approaches can be seen moving down and getting darker from the generally yellow top right corner.

As for negative mode, the mean candidate numbers per challenge cluster were calculated (left to right): magenta (5297), cyan (1029), red (886), black (1534), blue (978), green (722), with an overall mean of 1281.

The combinatorial fragmenters and CFM also performed well on the dark blue challenge cluster (second cluster from right)—in contrast both MS-FINDER and the CSI IOKR methods struggled for these challenges, shown with the yellow to red coloring in the heat map.

The large dark blue patch in the middle of the heat map indicates that for the majority of challenges, largely those in the black challenge cluster (top, middle), both the metadata but also the more extensive training data in positive mode for the machine learning approaches ensured that many Top 1 ranks were achieved.

The challenge clusters revealed some interesting patterns: four small clusters contained challenges that were problematic for different approaches.

Investigations into other parameters that may influence the challenge clusters, such as number of peaks in the spectra, revealed similarly inconclusive results.

The compounds in the dark blue and green challenge clusters are likely not to be covered too well in the training data for CSI IOKR.

Thirty eight percent of the cluster transcripts (including nu2 m and nua4l) showed a general up-regulation by both low and high calcium challenges (cluster 12 h-6, Figure 2).

The use of a storage area network (SAN) with Linux opens possibilities for scalable and affordable large data storage and poses a new challenge for cluster computing.