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In this paper, we extend the simple idea that contigs have better annotation performance than reads by using binning results, i.e., clusters, to perform annotation.
The new speaker will use transformations from all clusters to perform adaptation.
Unlike Google's MapReduce, Phoenix uses threads instead of clusters to perform the Map and Reduce tasks.
Using a similar method, we picked K = 5 as the optimal number of clusters to perform further analyses.
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Second, autoNOE-Rosetta uses a highly parallelizable iterative algorithm run on a computer cluster to perform assignment of long-range NOEs alongside the structure determination process.
Once defined the HPC cluster to perform the workflow, the sequential applications runs on cluster front-end and parallel applications on a defined number of nodes of a given HPC cluster.
It does so by properly allocating anchor nodes to different clusters during the network formation phase, and by allowing peers in the same cluster to perform ranging with each other.
However, since in this work we impose a condition of at least two MTs per cluster (to perform the combination between the short-range and the long-range measurements), the condition on the maximum allowed number of clusters is removed.
For hierarchical clustering analysis, we used clustering software Cluster 3.0 to perform complete linkage hierarchical clustering on genes, using uncentered Pearson's correlation as the distance measure.
In this paper, we extend the original Kohonen SOM network to include a contiguity-constrained clustering method to perform clustering based on the output map generated by the network.
The proposed technique focuses on a QT based fast 2-dimensional hierarchical clustering algorithm to perform clustering.
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