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The free texts occurred in TTD, Diseasome and other sources were mapped to disease ontology using string matching algorithms.
Spatially, seven concentration zones of water sources were mapped in Karamoja sub-region (Fig. 2) with a total of 1,271 water sources with 58%% of these being boreholes.
All conserved elements from both sources were mapped to the nearest gene with Galaxy [62] using appropriate UCSC human genome build, hg17 for CNS [19] and hg18 for phastCons elements.
All protein and gene identifiers used by these sources were mapped to UniProtKB accession numbers.
Households and water sources were mapped using open-source geospatial software.
Potential sources of bias identified from these sources were mapped and reviewed by an expert group.
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Known water sources are mapped using the Geographic Information System software, ArcGIS.
Because all model data sources are mapped into a common UniProt reference system, additional queries such as PFAM domain annotation are possible, even if the underlying model resource does not offer this functionality.
Data from this source were mapped to the International Classification of Diseases to enhance comparability of findings [ 24].
Each user's binary data from the source is mapped onto complex-valued symbols d l and spread in the frequency domain using a user-specific Walsh-Hadamard spreading code.
In a system with bandwidth expansion (b=frac {L}{M}), a vector of M samples X from the source is mapped to L analog channel symbols Y=α F X where F is a UTF and α is chosen such that the variance of the channel symbols is (sigma _{Y}^{2}= {mathcal {P}}_{T}).
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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