↓ Skip to main content

AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders

Overview of attention for article published in BMC Bioinformatics, April 2021
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

twitter
9 tweeters

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
26 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders
Published in
BMC Bioinformatics, April 2021
DOI 10.1186/s12859-021-04127-2
Pubmed ID
Authors

Seyedeh Zahra Sajadi, Mohammad Ali Zare Chahooki, Sajjad Gharaghani, Karim Abbasi

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 42%
Student > Doctoral Student 2 8%
Student > Bachelor 2 8%
Student > Master 2 8%
Professor 1 4%
Other 3 12%
Unknown 5 19%
Readers by discipline Count As %
Computer Science 6 23%
Pharmacology, Toxicology and Pharmaceutical Science 4 15%
Engineering 4 15%
Biochemistry, Genetics and Molecular Biology 3 12%
Social Sciences 1 4%
Other 3 12%
Unknown 5 19%

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 02 May 2021.
All research outputs
#11,729,005
of 21,025,129 outputs
Outputs from BMC Bioinformatics
#3,427
of 6,859 outputs
Outputs of similar age
#142,465
of 332,171 outputs
Outputs of similar age from BMC Bioinformatics
#19
of 50 outputs
Altmetric has tracked 21,025,129 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,859 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 48th percentile – i.e., 48% 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 332,171 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 56% of its contemporaries.
We're also able to compare this research output to 50 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 62% of its contemporaries.