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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Computing tumor trees from single cells
|
|---|---|
| Published in |
Genome Biology, May 2016
|
| DOI | 10.1186/s13059-016-0987-z |
| Pubmed ID | |
| Authors |
Alexander Davis, Nicholas E. Navin |
| Abstract |
Computational methods have been developed to reconstruct evolutionary lineages from tumors using single-cell genomic data. The resulting tumor trees have important applications in cancer research and clinical oncology.Please see related Research articles: http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0929-9 and http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0936-x . |
X Demographics
The data shown below were collected from the profiles of 20 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 States | 5 | 25% |
| United Kingdom | 3 | 15% |
| Belgium | 2 | 10% |
| Austria | 1 | 5% |
| Unknown | 9 | 45% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 50% |
| Scientists | 9 | 45% |
| Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 138 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 1% |
| Korea, Republic of | 1 | <1% |
| Spain | 1 | <1% |
| Germany | 1 | <1% |
| Unknown | 133 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 35 | 25% |
| Researcher | 30 | 22% |
| Student > Master | 16 | 12% |
| Student > Bachelor | 9 | 7% |
| Student > Doctoral Student | 8 | 6% |
| Other | 17 | 12% |
| Unknown | 23 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 48 | 35% |
| Biochemistry, Genetics and Molecular Biology | 33 | 24% |
| Computer Science | 20 | 14% |
| Medicine and Dentistry | 6 | 4% |
| Mathematics | 4 | 3% |
| Other | 3 | 2% |
| Unknown | 24 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 15 July 2017.
All research outputs
#3,222,521
of 25,374,917 outputs
Outputs from Genome Biology
#2,340
of 4,467 outputs
Outputs of similar age
#53,821
of 351,833 outputs
Outputs of similar age from Genome Biology
#55
of 84 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 47th percentile – i.e., 47% 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 351,833 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 84% of its contemporaries.
We're also able to compare this research output to 84 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.