↓ Skip to main content

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
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

twitter
4 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
33 Dimensions

Readers on

mendeley
51 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 51 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 48 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 24%
Student > Ph. D. Student 6 12%
Student > Master 6 12%
Student > Bachelor 4 8%
Professor 4 8%
Other 6 12%
Unknown 13 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 18%
Agricultural and Biological Sciences 9 18%
Chemistry 4 8%
Pharmacology, Toxicology and Pharmaceutical Science 4 8%
Computer Science 2 4%
Other 7 14%
Unknown 16 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 03 December 2023.
All research outputs
#6,767,899
of 24,930,865 outputs
Outputs from BMC Systems Biology
#214
of 1,129 outputs
Outputs of similar age
#75,717
of 320,714 outputs
Outputs of similar age from BMC Systems Biology
#7
of 59 outputs
Altmetric has tracked 24,930,865 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,129 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 81% 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 320,714 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.