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Approaches to the elucidation of gene regulatory networks have often relied on the use of clustering methodologies, grouping genes on the basis of expression patterns over time, treatments and/or tissues.
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Using a consensus of clustering methodology and modelling techniques, we have developed a clinically based classification of BC based on 10 biomarkers (Green et al, 2013).
Thus, using our clustering methodologies, we found no significant expansion of gene families within Agave species suggestive of adaptation to xeric environments.
We propose a methodology based on the use of clustering techniques derived from data analysis and multi-attribute decision analysis methods aiming at purposeful multidimensional poverty measurement.
The choice of methodology is a crucial issue in the use of clustering methods to examine structure in a given data set.
In terms of the clustering methodologies used here, the most surprising result was the high degree of biological information found using the FBPA clustering versus STEM clustering across both cell treatments, despite roughly equivalent computational evaluations.
Recent studies report favourably on the use of cluster randomisation as an appropriate methodology for use in this patient group.
In this work, second order estimation of GCM has been combined with the first order estimation of GCM using the property clustering methodology in order to increase the accuracy of the property predictions.
Thus, the NSCLC clusters identified using our clustering methodology are associated with different disease outcomes, suggesting that these genomic clusters represent clinically distinct subgroups of the disease.
Thus, we identified genomic subgroups of NSCLC using the unsupervised clustering methodology above and selected representative cell lines for each subtype.
America's use of cluster bombs will be studied.
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