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Under the first approach for data integration, we use administrative metadata [ 26, 27] from the financial system and the medication dispensing system, to create automated crosswalks between systems.
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For data integration we used Factor Analysis (FA, [ 5]) applied directly on biological data without any a priori hypothesis.
For data integration, we also created classes for representing Gene Index [ 22] and InterProScan [ 19] annotation.
For the integration we used Gauss Legendre quadratures.
Annotated information is required for data integration using matching identifiers, e.g. two microarray experiments on different platforms can be compared by mapping the vendor-specific probe ids to ENSEMBL gene ids.
While interchangeable for many cheminformatics purposes there have been no studies on the inconsistency of these structure identifiers due to various approaches for data integration, including the use of different software and different rules for structure standardisation.
Figure 4 shows the data integration workflow we used to populate the ontology.
The resulting controlled vocabularies are useful for data integration and disambiguation, e.g., terms could be used as keywords for scholarly works, enabling "similar" and/or "relevant" scholarly works to be identified.
The main benefit of this approach is that it sharply increases the potential for data integration, where two datasets are joined using a common identifier as the pivot point.
In the absence of consistently used pharmacy vocabularies in historical/legacy data, automated metadata-based methods can be used for data integration, and a combination of both techniques would allow the creation of large, longitudinal datasets which can be used in research.
Base calling, quality trimming and assembly of the raw sequence reads into contigs were done using the PHRED/PHRAP/CONSED package [ 20, 21] and GAP4 [ 22] from the Staden package was used for data integration and sequence finishing.
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