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Exact(1)
Data are matched to a species through syntactic equivalence between the query term and the external data source.
Similar(58)
OKAPI BM25 [11] uses a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document, regardless of the inter-relationship between the query terms within a document.
This is due to the fact that when the length of the query increases, there is less confusion between the query terms, because these typically differ to a great extent and hence a better performance is obtained.
The user can also view snippets from MEDLINE to get textual evidence of associations between the query terms and the concepts.
Okapi BM25 [ 2] is a state-of-the-art retrieval function used in document retrieval, which is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document, regardless of the relationship between the query terms within a document (e.g., their relative proximity).
Publications supporting the relationship between a gene and the query term are listed under the gene.
Exact: the query term must match an attribute perfectly.
Summing the tf-idf's of the query terms yields a simple measure of document relevance.
The query terms used are listed in the Appendix.
In April 2011, when the ATM was used to match some query terms with a MeSH term, the resulting modified query was different if the query terms matched with the preferred term or an entry term or a UMLsynonymnym [ 5].
In query outputs a table is displayed including the most relevant information for each prediction matching the query terms.
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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