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The two proposed hybrid ensemble methods, G-NCL and MNCE, are compared with their constituent methods, ME and NCL, in solving several benchmark problems.
In order to determine the evolutionary relationships between the P. gingivalis rag locus and homologous sequences, the phylogenetic trees were reconstructed by two different methods (ME and NJ methods) with high bootstrap values.
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For all three methods (Me-RDA, HMe-RDA, HELP Cocktail), 74% of the 23 sequences tested were validated to be partially methylated or hydroxymethylated.
Studies by Ikeda et al. [ 26], compared the effectiveness of several highly sensitive PCR methods (ME-PCR, PNA LNA PCR clamp and PCR invader) to detect EGFR mutations in paraffin-embedded tumour sections, frozen cytology specimens obtained by bronchoscopy (washing and brushing) or from malignant pleural effusions.
In this work, we present and examine the multi-element probabilistic collocation method (ME-PCM), which is a generalized form of the probabilistic collocation method.
A multi-element probabilistic collocation method (ME-PCM) on sparse grids is employed to solve the stochastic Euler equations while a WENO scheme is used to discretize the equations in two spatial dimensions.
To address this problem, we have extended a retrotransposon-based capture and sequence method (ME-Scan [mobile element scanning]) to identify insertions belonging to the Ves family of short interspersed elements (SINEs) across seven species of the bat genus Myotis.
The model prototypes were used as initial parameters of linguistic motivated methods L-ME and T-ME.
This is consistent with slightly better performance of likelihood-based methods over ME methods (e.g. [ 36]).
The Method Engineering (ME) discipline emerged as a response to the need for methods that are better adapted to context.
The method error (ME) was estimated using Dahlberg's formula [13]: mathrm{ME}=sqrt{frac{{displaystyle sum {d}^2}}{2n}}.
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