| Title |
A neural network approach to chemical and gene/protein entity recognition in patents
|
|---|---|
| Published in |
Journal of Cheminformatics, December 2018
|
| DOI | 10.1186/s13321-018-0318-3 |
| Pubmed ID | |
| Authors |
Ling Luo, Zhihao Yang, Pei Yang, Yin Zhang, Lei Wang, Jian Wang, Hongfei Lin |
X Demographics
The data shown below were collected from the profiles of 8 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 States | 2 | 25% |
| France | 1 | 13% |
| Spain | 1 | 13% |
| Israel | 1 | 13% |
| Unknown | 3 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 50% |
| Scientists | 3 | 38% |
| Science communicators (journalists, bloggers, editors) | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 37 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 16% |
| Student > Doctoral Student | 4 | 11% |
| Student > Bachelor | 4 | 11% |
| Student > Ph. D. Student | 4 | 11% |
| Lecturer | 2 | 5% |
| Other | 4 | 11% |
| Unknown | 13 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 12 | 32% |
| Business, Management and Accounting | 4 | 11% |
| Engineering | 2 | 5% |
| Agricultural and Biological Sciences | 1 | 3% |
| Immunology and Microbiology | 1 | 3% |
| Other | 3 | 8% |
| Unknown | 14 | 38% |
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 25 December 2018.
All research outputs
#7,443,076
of 25,761,363 outputs
Outputs from Journal of Cheminformatics
#572
of 981 outputs
Outputs of similar age
#141,979
of 446,558 outputs
Outputs of similar age from Journal of Cheminformatics
#15
of 24 outputs
Altmetric has tracked 25,761,363 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 981 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 10.0. This one is in the 41st percentile – i.e., 41% 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 446,558 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 68% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.