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Mining basic active structures from a large-scale database

Overview of attention for article published in Journal of Cheminformatics, March 2013
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1 X user
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1 Google+ user

Citations

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5 Dimensions

Readers on

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31 Mendeley
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Title
Mining basic active structures from a large-scale database
Published in
Journal of Cheminformatics, March 2013
DOI 10.1186/1758-2946-5-15
Pubmed ID
Authors

Naoto Takada, Norihito Ohmori, Takashi Okada

Abstract

The Pubchem Database is a large-scale resource for chemical information, containing millions of chemical compound activities derived by high-throughput screening (HTS). The ability to extract characteristic substructures from such enormous amounts of data is steadily growing in importance. Compounds with shared basic active structures (BASs) exhibiting G-protein coupled receptor (GPCR) activity and repeated dose toxicity have been mined from small datasets. However, the mining process employed was not applicable to large datasets owing to a large imbalance between the numbers of active and inactive compounds. In most datasets, one active compound will appear for every 1000 inactive compounds. Most mining techniques work well only when these numbers are similar.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
China 1 3%
Germany 1 3%
Unknown 28 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 23%
Researcher 5 16%
Professor > Associate Professor 4 13%
Student > Master 4 13%
Professor 3 10%
Other 5 16%
Unknown 3 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 23%
Chemistry 6 19%
Computer Science 4 13%
Medicine and Dentistry 4 13%
Biochemistry, Genetics and Molecular Biology 2 6%
Other 5 16%
Unknown 3 10%
Attention Score in Context

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 March 2013.
All research outputs
#14,165,787
of 22,703,044 outputs
Outputs from Journal of Cheminformatics
#700
of 828 outputs
Outputs of similar age
#112,366
of 196,550 outputs
Outputs of similar age from Journal of Cheminformatics
#12
of 15 outputs
Altmetric has tracked 22,703,044 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 828 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 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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 196,550 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.