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Classification of alkaloids according to the starting substances of their biosynthetic pathways using graph convolutional neural networks

Overview of attention for article published in BMC Bioinformatics, July 2019
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
52 Mendeley
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Title
Classification of alkaloids according to the starting substances of their biosynthetic pathways using graph convolutional neural networks
Published in
BMC Bioinformatics, July 2019
DOI 10.1186/s12859-019-2963-6
Authors

Ryohei Eguchi, Naoaki Ono, Aki Hirai Morita, Tetsuo Katsuragi, Satoshi Nakamura, Ming Huang, Md. Altaf-Ul-Amin, Shigehiko Kanaya

Twitter Demographics

The data shown below were collected from the profiles of 2 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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 8 15%
Researcher 6 12%
Student > Master 6 12%
Student > Doctoral Student 4 8%
Other 3 6%
Other 9 17%
Unknown 16 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 19%
Chemistry 7 13%
Computer Science 5 10%
Agricultural and Biological Sciences 4 8%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Other 6 12%
Unknown 18 35%

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 14 July 2019.
All research outputs
#11,772,634
of 15,432,447 outputs
Outputs from BMC Bioinformatics
#4,422
of 5,639 outputs
Outputs of similar age
#177,009
of 261,902 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 28 outputs
Altmetric has tracked 15,432,447 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,639 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 15th percentile – i.e., 15% 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 261,902 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.