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Therefore, we conclude our method is suitable for autofocusing data with different SA patterns, which is an extraordinary superiority to autofocusing methods available.
To conclude, our method offers the possibility of using MRI volume for both source localization and spatial localization of EEG sensors.
To conclude, our method in most cases can achieve similar performance to or sometimes outperform other state-of-the-art methods for identifying similar or redundant motifs in a database as well as for clustering similar motifs of structurally or evolutionarily related TFs.
Finally, we conclude our method and discuss some possible improvements for the future.
To conclude, our method is highly efficient for evaluating hypothetical proteins on the basis of DNA/RNA-binding function.
When compared with results from our method, we conclude our method outperforms the other four methods as it provides the most consistent results across the anatomical components as well as the most balanced results between sensitivity and specificity.
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We can conclude that our method outperforms F5, MB1, and Outguess in image quality.
We conclude that our method has computational complexity that scales linearly with the number of interactions.
Hence, we can conclude that our method calibrates the extrinsic parameters more accurately.
Therefore, we can conclude that our method estimates the effects due to uncertainties of parameters fairly well.
We therefore conclude that our method works well as a real-time system with fewer than 300 particles.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com