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The orthology based method achieved prediction accuracy of 83.86%, and among the novel predictions, it only covered 56.35% of our novel discoveries.
In contrast, with the same testing dataset, the Majority Rule algorithm, the simple domain based method, and orthology based method achieved only 59.50%, 61.98%, and 83.86% in accuracy, respectively.
Comprehensive experiments show that this feature fusion based method achieves much better performances compared to traditional methods.
We have shown that the proposed quartet based methods achieved good sensitivity and specificity on simulated data.
The proposed TSCC-based method achieved a better subjective quality than the WB audio signals on the whole.
The phylogenetic tree of 54 prokaryotic taxa generated by our DCNs-alignment-based method achieved a 93.45% similarity score compared to the Bergey's taxonomy.
The simulation study shows that spectral correlation and short-time RMS based methods achieves similar level of accuracy as Hilbert transform.
The BLAST based methods achieve high accuracy on the non-duplicated nodes since the sequences of nodes are not changed.
The CRF based methods achieve the best results, which shows that the edge feature function used in CRF based methods is helpful for utilizing the structural information to discriminate the biological similar nodes.
Abundance values based on our "frequency-weighted read count" method achieve a very high correlation (Pearson correlation coefficient 0.99) with the true abundance values of all the KO-families present in our data set (details in Methods section).
Simulation results demonstrate that bulk-driven amplifier based on the proposed method achieves an 8 times slew rate and a 4 times bandwidth improvement with only a negligible increase in power consumption.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com