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Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

Overview of attention for article published in BMC Cancer, August 2019
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)

Mentioned by

news
1 news outlet
twitter
11 tweeters

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
8 Mendeley
Title
Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA
Published in
BMC Cancer, August 2019
DOI 10.1186/s12885-019-6003-8
Pubmed ID
Authors

Nathan Wan, David Weinberg, Tzu-Yu Liu, Katherine Niehaus, Eric A. Ariazi, Daniel Delubac, Ajay Kannan, Brandon White, Mitch Bailey, Marvin Bertin, Nathan Boley, Derek Bowen, James Cregg, Adam M. Drake, Riley Ennis, Signe Fransen, Erik Gafni, Loren Hansen, Yaping Liu, Gabriel L. Otte, Jennifer Pecson, Brandon Rice, Gabriel E. Sanderson, Aarushi Sharma, John St. John, Catherina Tang, Abraham Tzou, Leilani Young, Girish Putcha, Imran S. Haque

Twitter Demographics

The data shown below were collected from the profiles of 11 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 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 138%
Student > Ph. D. Student 6 75%
Student > Bachelor 3 38%
Student > Master 3 38%
Other 2 25%
Other 4 50%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 88%
Agricultural and Biological Sciences 6 75%
Medicine and Dentistry 5 63%
Computer Science 4 50%
Unspecified 2 25%
Other 5 63%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 13 September 2019.
All research outputs
#1,025,178
of 13,617,861 outputs
Outputs from BMC Cancer
#195
of 5,103 outputs
Outputs of similar age
#28,760
of 228,346 outputs
Outputs of similar age from BMC Cancer
#1
of 1 outputs
Altmetric has tracked 13,617,861 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,103 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done particularly well, scoring higher than 96% 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 228,346 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them