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We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task.
Thorough analysis of these datasets, either manually by expert scientists (Expert Learning) or automatically by computer algorithms (Machine Learning) [1], can provide a better understanding of the structure activity relationships.
The image data are subsequently analyzed manually by expert analysts.
The orthologs clusters were then curated manually by expert knowledge and literature search.
All annotation submitted by community members should be vetted manually by expert annotators, and subsequently incorporated into the assembly, with an indication of who provided the data.
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Today, BCS is performed manually by experts.
Traditionally, questions have been constructed manually by experts.
In general, analysis of those medical big data is performed manually by experts to identify and understand the abnormalities.
Section 5.1 presents the number of code smells identified manually by experts in all versions of each system.
It may be conducted manually by experts determining the sentiment of a given text, automatically using machine learning algorithms and statistical methods, and by crowdsourcing.
Additional data for chemical structures has been collected from various public sources (e.g. Chemical Identifier Resolver [63], ChemIDplus [64], PubChem [3]) and further checked manually by experts.
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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