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Deep Learning-Based Structure-Activity Relationship Modeling for Multi-Category Toxicity Classification: A Case Study of 10K Tox21 Chemicals With High-Throughput Cell-Based Androgen Receptor Bioassay…

Overview of attention for article published in Frontiers in Physiology, August 2019
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

Mentioned by

patent
1 patent

Citations

dimensions_citation
41 Dimensions

Readers on

mendeley
57 Mendeley
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Title
Deep Learning-Based Structure-Activity Relationship Modeling for Multi-Category Toxicity Classification: A Case Study of 10K Tox21 Chemicals With High-Throughput Cell-Based Androgen Receptor Bioassay Data
Published in
Frontiers in Physiology, August 2019
DOI 10.3389/fphys.2019.01044
Pubmed ID
Authors

Gabriel Idakwo, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, Ping Gong

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 57 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 26%
Student > Master 6 11%
Student > Bachelor 5 9%
Researcher 5 9%
Professor 2 4%
Other 3 5%
Unknown 21 37%
Readers by discipline Count As %
Chemistry 9 16%
Environmental Science 5 9%
Pharmacology, Toxicology and Pharmaceutical Science 4 7%
Engineering 3 5%
Biochemistry, Genetics and Molecular Biology 3 5%
Other 10 18%
Unknown 23 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 20 May 2021.
All research outputs
#7,656,056
of 23,308,124 outputs
Outputs from Frontiers in Physiology
#3,814
of 14,041 outputs
Outputs of similar age
#134,274
of 343,049 outputs
Outputs of similar age from Frontiers in Physiology
#110
of 374 outputs
Altmetric has tracked 23,308,124 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,041 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has gotten more attention than average, scoring higher than 72% 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 343,049 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 55% of its contemporaries.
We're also able to compare this research output to 374 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 67% of its contemporaries.