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A neural network approach to chemical and gene/protein entity recognition in patents

Overview of attention for article published in Journal of Cheminformatics, December 2018
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
8 X users

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
37 Mendeley
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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

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.
Mendeley readers

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 > Ph. D. Student 4 11%
Student > Bachelor 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

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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
#7,775,401
of 25,539,438 outputs
Outputs from Journal of Cheminformatics
#583
of 971 outputs
Outputs of similar age
#146,823
of 444,821 outputs
Outputs of similar age from Journal of Cheminformatics
#16
of 24 outputs
Altmetric has tracked 25,539,438 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 971 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.0. This one is in the 39th percentile – i.e., 39% 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 444,821 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 66% 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.