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There are several algorithms available for computing the MAP estimate.
Although there are several algorithms available for this task, their classification performance remains a major concern.
From several algorithms available [ 34], we chose the expectation maximization method MEME (version 3.0.8) [ 35] and the Gibbs Sampling method AlignACE [ 36].
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Several algorithms are available to classify landscapes at different scales.
Several algorithms were available to perform the analysis, such as the K-Means (MacQueen, 1967), the COBWEB Fisherr, 1987), and the DBSCAN (Ester et al., 1996).
Several algorithms are available to define which data are blinded (random selection, balanced random selection, experimental designs on the dependent or independent variables) where the employed algorithm influences the validation results.
Several algorithms are available to solve this task, and we focus here on three of the most commonly used methods: extended Infomax [27, 28] as implemented in EEGLab [38], FastICA [29, 30] and SOBI/TDSEP [31, 32].
For the model plant Arabidopsis thaliana, large-scale data sets have been generated at many different biological scales [8] and several algorithms are available to identify associations among these levels [9], [10] towards a systems understanding of biological processes.
Despite several algorithms being available for different AHDs in different settings, none of them have been fully validated in the general population.
Taxonomic assignment can be performed by MG-RAST only with BLAT [ 17], while several algorithms are available in the QIIME pipeline [ 34, 35] (Table 1).
Several algorithms are available that can solve RMQ in constant time, after a linear preprocessing step, e.g. Fischer and Heun (2007).
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