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Pizzuti, C. & Rombo, S. E. Algorithms and tools for protein-protein interaction networks clustering, with a special focus on population-based stochastic methods.
Three different clustering methods for constructing the prior GM are considered: (i) a standard kernel density estimation, (ii) clustering with a specified mixture component size, and (iii) adaptive clustering (with a variable GM size).
In this paper, a novel soft subspace clustering with a multi-objective evolutionary approach (MOEASSC) is proposed to this problem.
In traditional industrialization, manufacturing industries are clustering with a large amount of carbon dioxide (CO2) emissions which has been polluting the environment heavily and directly caused global climate change.
Clustering with a constant threshold may produce several clusters similar to each other.
As given in Table 1, clustering with a radius of 0.6 provides the hybrid TLBOFIS with the highest accuracy.
A high similarity was observed for LH clone sequences clustering with a WS/DGAT sequence of Kribbella flavida (Pukall et al. 2010).
The similarities of Bradyrhizobium D-carbamoylases clustering with a hydantoinase to the Aminobacter sp. carbamoylases are 71 74% while the similarities to the other carbamoylases are between 46 and 74%.
The sequence similarity of the Aminobacter sp. dihydropyrimidinases to the Bradyrhizobium hydantoinases not forming a cluster with a D-carbamoylase is again higher (49 78%) than to the hydantoinases clustering with a D-carbamoylase (47 51%).
Causal comorbidity describes disease clustering with a pathophysiological relation between the different diseases (e.g. shared risk factors).
Some cases of mislabeling were detected through identification of individuals clustering with a high coefficient of membership (over 0.70) in a different cluster from most of the individuals collected from the same expected locality (according to passport data).
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