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Centrality analysis, path analysis, cluster analysis, etc. are used to identify the key papers of paradigm shifts, emerging fields, relatively important clusters and works respectively.
These identified putative proteins were then used to identify functionally important clusters and common biomarkers among plasma/serum and CSF proteome.
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The least important clusters were 'marketing'undefinedned' and 'broad utility' (figure 2).
We introduce several important clustering schemes and energy models in Section 2. In Section 3, we introduce the overall procedures of the clustering scheme, assumptions for modeling, and formulate the problem.
The major limitation of Moran's I is that it only measures spatial clustering (spatial autocorrelation) at a global scale, but cannot detect important clusters at a local scale and determine the spatial patterns for the specific locations.
These seven clusters were presented to the visiting inspectors and co-inspectors and they were asked to rank the three most important and the three least important clusters based on their professional experience.
The three most important clusters were: (i) 'solution orientation', (ii) 'policy relevance' and (iii) 'policy impact'.
This resulted in the identification of 6 important clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes.
Important clusters of activation were observed with the emotional memory paradigm in HC and MCI in MTL and medial frontal regions.
The psychosocial domain clusters (Affective, Domestic, and Social) were more likely to be selected as the least important clusters.
Some important clusters seem to appear, however it is difficult to asses the relevance of individual nodes.
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