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Multiple configurations were examined and the local energy landscape was characterized to determine cluster mobility and the potential for interactions with the surface.
In this paper we present the method TreeSOM and a set of tools to perform unsupervised SOM cluster analysis, determine cluster confidence and visualize the result as a tree facilitating comparison with existing hierarchical classifiers.
Both carbon chain length and bond type determine cluster membership.
Additionally, an important observation in our study was that the SNPs/genes that determine cluster formation in CD do not appear to group in specific pathways: eg TNFSF15 and IRGM, which explain cluster B (Figure 1B) are – to the best of our knowledge – not part of one and the same pathway.
Monte-Carlo alpha simulations were used to determine cluster significance at a corrected threshold of p <.05 [ 60].
The Pearson correlation coefficient between each Unigene and each template profile was used to determine cluster membership: correlation measures greater than 0.75 corresponded to a good match.
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The method is based on training data that is analyzed offline via Singular Value Decomposition to determine clustering vectors that can be utilized for state estimation in real-time.
In order to reduce the dimension and determine clustering of chronobiological variables, a variable cluster analysis (proc varclus) was conducted.
Figure 11b verifies the ability of a one-class support vector machine (OCSVM) [26], to determine clusters corresponding to the NPD class, as shown in Fig. 11b.
We used Euclidean distance to determine clusters.
Hierarchical bi-clustering was applied to determine clusters in these matrices (see Materials and Methods for details).
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