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KCF-S: KEGG Chemical Function and Substructure for improved interpretability and prediction in chemical bioinformatics

Overview of attention for article published in BMC Systems Biology, December 2013
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
42 Mendeley
citeulike
1 CiteULike
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Title
KCF-S: KEGG Chemical Function and Substructure for improved interpretability and prediction in chemical bioinformatics
Published in
BMC Systems Biology, December 2013
DOI 10.1186/1752-0509-7-s6-s2
Pubmed ID
Authors

Masaaki Kotera, Yasuo Tabei, Yoshihiro Yamanishi, Yuki Moriya, Toshiaki Tokimatsu, Minoru Kanehisa, Susumu Goto

Abstract

In order to develop hypothesis on unknown metabolic pathways, biochemists frequently rely on literature that uses a free-text format to describe functional groups or substructures. In computational chemistry or cheminformatics, molecules are typically represented by chemical descriptors, i.e., vectors that summarize information on its various properties. However, it is difficult to interpret these chemical descriptors since they are not directly linked to the terminology of functional groups or substructures that the biochemists use.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 2%
France 1 2%
Switzerland 1 2%
Unknown 39 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 26%
Student > Master 6 14%
Student > Ph. D. Student 5 12%
Student > Bachelor 4 10%
Professor 4 10%
Other 5 12%
Unknown 7 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 24%
Agricultural and Biological Sciences 9 21%
Pharmacology, Toxicology and Pharmaceutical Science 4 10%
Chemistry 4 10%
Engineering 2 5%
Other 4 10%
Unknown 9 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 27 October 2018.
All research outputs
#7,864,546
of 14,571,155 outputs
Outputs from BMC Systems Biology
#414
of 1,090 outputs
Outputs of similar age
#105,311
of 255,697 outputs
Outputs of similar age from BMC Systems Biology
#21
of 47 outputs
Altmetric has tracked 14,571,155 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,090 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 255,697 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.
We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.