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Machine-learning techniques for the prediction of protein–protein interactions

Overview of attention for article published in Proceedings: Plant Sciences, August 2019
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
57 Dimensions

Readers on

mendeley
99 Mendeley
Title
Machine-learning techniques for the prediction of protein–protein interactions
Published in
Proceedings: Plant Sciences, August 2019
DOI 10.1007/s12038-019-9909-z
Pubmed ID
Authors

Debasree Sarkar, Sudipto Saha

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 99 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 15%
Student > Bachelor 12 12%
Student > Master 7 7%
Student > Doctoral Student 6 6%
Researcher 6 6%
Other 9 9%
Unknown 44 44%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 16%
Engineering 8 8%
Computer Science 8 8%
Agricultural and Biological Sciences 7 7%
Mathematics 3 3%
Other 11 11%
Unknown 46 46%
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 05 December 2020.
All research outputs
#8,540,769
of 25,385,509 outputs
Outputs from Proceedings: Plant Sciences
#242
of 975 outputs
Outputs of similar age
#136,300
of 338,412 outputs
Outputs of similar age from Proceedings: Plant Sciences
#15
of 39 outputs
Altmetric has tracked 25,385,509 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 975 research outputs from this source. They receive a mean Attention Score of 3.8. This one is in the 45th percentile – i.e., 45% 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 338,412 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 53% of its contemporaries.
We're also able to compare this research output to 39 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 56% of its contemporaries.