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To do so, we created a workflow that allowed the pipeline processing of literature data and biological data, extracted from Web of Science (WoS) or PubMed but also from Gene Expression Omnibus (GEO), Gene Ontology (GO), Uniprot, HomoloGene, TcoF-DB and TFe (TF encyclopedia).
We searched the MEDLINE database via PubMed and required dual review of full text articles and of data extracted from studies.
First, Chinese herbal ingredients in vivo metabolism data were extracted from PubMed [20] literature by searching with key words: metabolism, metabolite, biotransformation, metabolic, CHM, Chinese herbal medicine, in vivo.
A compendium of data extracted from the scientific literature.
Data extracted from MEMWE website.
Source: data extracted from [44].
Data extracted from 13 studies.
Data extracted from CHKS database.
To catalog the existing knowledge in the field, we used text mining to generate the Sjögren's Syndrome Knowledge Base (SSKB) of published gene/protein data, which were extracted from PubMed using text mining of over 7,700 abstracts and listing approximately 500 potential genes/proteins.
The raw data for SSKB was extracted from PubMed [ 19]) using the text mining program EBIMed (http://www.ebi.ac.uk/Rebholz-srv/ebimed/) [ 20] with the search term "Sjogren's Syndrome" restricted to "MeshHeadingsList".
Data in the initial release were extracted from PubMed by the authors; a senior biologist collected studies and another biologist integrated its entries into the database.
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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