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When examining these datasets, and then turning to applying our methods on large blocks of genomic data, there seems to be a "contrast" problem in the recognition of the start-of-coding region when working with the standard 1st order HMM (a "needle-in-the-haystack" problem).
Applying our methods on several microarray datasets, we have discovered many significantly activated TFs and their corresponding pathway segments, which are supported by evidence in the literature.
We then present experimental results obtained from applying our methods to a biological example and discuss applications of our algorithm towards sequential circuits.
Thus, for these experiments, we performed a sensitivity analysis, applying our methods both with no background kernel, and with one (based on all SNPs).
We encourage others to consider applying our methods and approaches in other sectors and continents to determine their usefulness and the extent of their transferability.
Applying our methods to 32 cancer-related microarray datasets, and 23 non-cancer related datasets, we derived 162 second-order clusters consisting of 224 network modules, activated either in cancer or in specific cancer subtypes.
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The rotation angle is estimated by applying our method using a polar coordinate system.
Finally, we will report the results from applying our method to a case study and discussing potential extensions.
After applying our method to the data, the negative correlation is evenly distributed over all replicates x ∈ [21, 36]×y ∈ [1,16].
We then introduced BW feature selection before applying our method.
Our immediate future work includes applying our method to additional entity types.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com