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To ameliorate the current state, we use the ensemble algorithm instead of single classifier.
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In [14] we showed, empirically, that the best alternative was to use the Ensemble algorithms (Alternative 2) for the classification problem.
We verified that the clusters obtained using the ensemble learning algorithm are robust to both the algorithm initialisation point and the error due to unstable housekeeping gene expression.
The MDock is automated molecular docking software for simultaneously docking dioscin with known/available three-dimension crystal structure against drug targets from PDTD with multiple protein structure/conformations downloaded from RCSB Protein Data Bank (PDB) by using the ensemble docking algorithm [ 24].
The results of unsupervised clustering using the ensemble-learning algorithm as applied to the normalised expression matrix VG are summarised in Table 3.
Taking a synthetic reservoir as an example, Li et al. proposed a numerical inversion method for estimation of the three-phase relative permeability curve using the ensemble Kalman filter algorithm for assisted history matching.
Figure 3 shows a toy example of Ensemble-1 and 2. For clarity, in the next sections we use the name annotation method for both pipelines and variants of the ensemble algorithm.
The experiment shows that by using the ensemble of signal, the proposed algorithm improved the PSNR of the original OMP-PKS by at least 0.34 dB.
The ensemble algorithm is illustrated in Algorithm 3. Ensemble classification is a method used to combine various basic classifiers that each has independent decision-making ability.
The ensemble algorithm markedly improved the number of significant GO terms: Ensemble-1 achieved 718, and Ensemble-2 achieved 650.
Figure 4A and 4B also show the gene coverage and the NGPG of the ensemble algorithm.
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Justyna Jupowicz-Kozak
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