| 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 |
X Demographics
The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 25% |
| United States | 2 | 17% |
| Ireland | 1 | 8% |
| Unknown | 6 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 67% |
| Scientists | 2 | 17% |
| Practitioners (doctors, other healthcare professionals) | 2 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 221 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 221 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 46 | 21% |
| Student > Ph. D. Student | 33 | 15% |
| Student > Master | 21 | 10% |
| Student > Bachelor | 13 | 6% |
| Student > Doctoral Student | 10 | 5% |
| Other | 26 | 12% |
| Unknown | 72 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 42 | 19% |
| Medicine and Dentistry | 26 | 12% |
| Agricultural and Biological Sciences | 22 | 10% |
| Computer Science | 20 | 9% |
| Engineering | 10 | 5% |
| Other | 22 | 10% |
| Unknown | 79 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 23. 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 30 May 2023.
All research outputs
#1,532,495
of 24,337,175 outputs
Outputs from BMC Cancer
#217
of 8,649 outputs
Outputs of similar age
#32,572
of 345,330 outputs
Outputs of similar age from BMC Cancer
#6
of 162 outputs
Altmetric has tracked 24,337,175 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,649 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 97% 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 345,330 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 90% of its contemporaries.
We're also able to compare this research output to 162 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 96% of its contemporaries.