↓ Skip to main content

Network machine learning maps phytochemically rich “Hyperfoods” to fight COVID-19

Overview of attention for article published in Human Genomics, January 2021
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#4 of 353)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

blogs
2 blogs
twitter
95 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
52 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
Network machine learning maps phytochemically rich “Hyperfoods” to fight COVID-19
Published in
Human Genomics, January 2021
DOI 10.1186/s40246-020-00297-x
Pubmed ID
Authors

Ivan Laponogov, Guadalupe Gonzalez, Madelen Shepherd, Ahad Qureshi, Dennis Veselkov, Georgia Charkoftaki, Vasilis Vasiliou, Jozef Youssef, Reza Mirnezami, Michael Bronstein, Kirill Veselkov

Twitter Demographics

The data shown below were collected from the profiles of 95 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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 17%
Student > Ph. D. Student 6 12%
Researcher 5 10%
Student > Doctoral Student 5 10%
Unspecified 4 8%
Other 9 17%
Unknown 14 27%
Readers by discipline Count As %
Medicine and Dentistry 12 23%
Computer Science 5 10%
Nursing and Health Professions 4 8%
Unspecified 4 8%
Pharmacology, Toxicology and Pharmaceutical Science 3 6%
Other 9 17%
Unknown 15 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 59. 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 May 2021.
All research outputs
#447,123
of 17,717,881 outputs
Outputs from Human Genomics
#4
of 353 outputs
Outputs of similar age
#17,260
of 401,685 outputs
Outputs of similar age from Human Genomics
#1
of 39 outputs
Altmetric has tracked 17,717,881 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 353 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one has done particularly well, scoring higher than 98% 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 401,685 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% 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 done particularly well, scoring higher than 99% of its contemporaries.