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The PV module analysis methods are an interesting field of research which will involve in the near future active cooperation between academia, laboratories, financial institutions, and manufacturers to define a new paradigm about data collection and accuracy [2].
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For a feasibility test of the proposed methodology, 46 blankets in a single sector were simulated; the calculation results of the parameters, such as mass flow, pressure drops, and temperature distribution in the multiple blanket modules showed that the multi-module analysis method can be used for efficient thermal-hydraulic analysis of the fusion DEMO reactor.
The modules generated by SSIM are found to be more significantly and specifically associated with biological functions than the results obtained from two methods, Module Analysis via Topology of Interactions and Similarity Sets (MATISSE) [ 12] and Interaction Component Models for Gene modules (ICMg) [ 14].
Many network-based analysis methods extract modules, which are sets of tightly connected nodes consisting of the input genes, and it is strongly expected that the genes in a module achieve a biological function in a coordinated manner.
To investigate the coexistence of the species in the IGP module, this study considers two factors: analysis methods and individual variation in adaptive behavior.
NAP conceived the idea, executed all computational and bioinformatic methods (KS statistics, module analysis with GSEA, GO analysis and network re-construction) and oversaw all aspects of the work and the manuscript.
Ulitsky et al. [ 56] also proposed a method called MATISSE (Module Analysis via Topology of Interactions and Similarity SEts) to grow a functional module from a set of seed proteins.
The database contains almost 250 000 modules that were generated using multiple analysis methods and integration of microarray expression data.
In GO enrichment analysis (Methods), all identified network modules were related to DNA or RNA metabolisms.
In the 'Analysis of Molecular-Interaction Map and Network Modules' section of this paper we use network analysis methods to understand the systemic interactions of molecules involved in RA.
Lastly, our functional prediction at both individual gene level and the module level is informative and reliable through using three complementary analysis methods.
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