| 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 |
| Pubmed ID | |
| Authors |
Ryohei Eguchi, Naoaki Ono, Aki Hirai Morita, Tetsuo Katsuragi, Satoshi Nakamura, Ming Huang, Md. Altaf-Ul-Amin, Shigehiko Kanaya |
X Demographics
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 145 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 145 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 20 | 14% |
| Student > Master | 16 | 11% |
| Researcher | 10 | 7% |
| Student > Ph. D. Student | 10 | 7% |
| Student > Doctoral Student | 6 | 4% |
| Other | 14 | 10% |
| Unknown | 69 | 48% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 18 | 12% |
| Chemistry | 11 | 8% |
| Computer Science | 8 | 6% |
| Agricultural and Biological Sciences | 6 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 5 | 3% |
| Other | 20 | 14% |
| Unknown | 77 | 53% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 13 January 2023.
All research outputs
#4,298,265
of 24,066,486 outputs
Outputs from BMC Bioinformatics
#1,585
of 7,498 outputs
Outputs of similar age
#81,409
of 349,695 outputs
Outputs of similar age from BMC Bioinformatics
#60
of 162 outputs
Altmetric has tracked 24,066,486 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,498 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 78% 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 349,695 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 162 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 63% of its contemporaries.