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A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder

Overview of attention for article published in Frontiers in Pharmacology, January 2020
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Mentioned by

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1 X user

Citations

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31 Dimensions

Readers on

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40 Mendeley
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Title
A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder
Published in
Frontiers in Pharmacology, January 2020
DOI 10.3389/fphar.2019.01592
Pubmed ID
Authors

Huiqing Wang, Jingjing Wang, Chunlin Dong, Yuanyuan Lian, Dan Liu, Zhiliang Yan

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 7 18%
Student > Ph. D. Student 5 13%
Researcher 4 10%
Student > Master 3 8%
Student > Postgraduate 2 5%
Other 5 13%
Unknown 14 35%
Readers by discipline Count As %
Engineering 7 18%
Computer Science 7 18%
Pharmacology, Toxicology and Pharmaceutical Science 3 8%
Biochemistry, Genetics and Molecular Biology 3 8%
Chemistry 2 5%
Other 3 8%
Unknown 15 38%
Attention Score in Context

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 12 February 2020.
All research outputs
#20,604,769
of 23,192,960 outputs
Outputs from Frontiers in Pharmacology
#10,401
of 16,608 outputs
Outputs of similar age
#377,150
of 451,488 outputs
Outputs of similar age from Frontiers in Pharmacology
#288
of 447 outputs
Altmetric has tracked 23,192,960 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 16,608 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 1st percentile – i.e., 1% 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 451,488 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 447 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.