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A chemical specialty semantic network for the Unified Medical Language System

Overview of attention for article published in Journal of Cheminformatics, May 2012
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Title
A chemical specialty semantic network for the Unified Medical Language System
Published in
Journal of Cheminformatics, May 2012
DOI 10.1186/1758-2946-4-9
Pubmed ID
Authors

C Paul Morrey, Yehoshua Perl, Michael Halper, Ling Chen, Huanying “Helen” Gu

Abstract

Terms representing chemical concepts found the Unified Medical Language System (UMLS) are used to derive an expanded semantic network with mutually exclusive semantic types. The UMLS Semantic Network (SN) is composed of a collection of broad categories called semantic types (STs) that are assigned to concepts. Within the UMLS's coverage of the chemical domain, we find a great deal of concepts being assigned more than one ST. This leads to the situation where the extent of a given ST may contain concepts elaborating variegated semantics.A methodology for expanding the chemical subhierarchy of the SN into a finer-grained categorization of mutually exclusive types with semantically uniform extents is presented. We call this network a Chemical Specialty Semantic Network (CSSN). A CSSN is derived automatically from the existing chemical STs and their assignments. The methodology incorporates a threshold value governing the minimum size of a type's extent needed for inclusion in the CSSN. Thus, different CSSNs can be created by choosing different threshold values based on varying requirements.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 5%
Netherlands 1 5%
Germany 1 5%
Unknown 16 84%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 21%
Researcher 3 16%
Student > Ph. D. Student 2 11%
Student > Bachelor 1 5%
Professor 1 5%
Other 4 21%
Unknown 4 21%
Readers by discipline Count As %
Computer Science 5 26%
Agricultural and Biological Sciences 2 11%
Arts and Humanities 1 5%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Biochemistry, Genetics and Molecular Biology 1 5%
Other 2 11%
Unknown 7 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 May 2012.
All research outputs
#15,243,549
of 22,665,794 outputs
Outputs from Journal of Cheminformatics
#743
of 825 outputs
Outputs of similar age
#104,474
of 163,915 outputs
Outputs of similar age from Journal of Cheminformatics
#8
of 8 outputs
Altmetric has tracked 22,665,794 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 825 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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