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.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Towards accurate characterization of clonal heterogeneity based on structural variation
|
|---|---|
| Published in |
BMC Bioinformatics, September 2014
|
| DOI | 10.1186/1471-2105-15-299 |
| Pubmed ID | |
| Authors |
Xian Fan, Wanding Zhou, Zechen Chong, Luay Nakhleh, Ken Chen |
| Abstract |
Recent advances in deep digital sequencing have unveiled an unprecedented degree of clonal heterogeneity within a single tumor DNA sample. Resolving such heterogeneity depends on accurate estimation of fractions of alleles that harbor somatic mutations. Unlike substitutions or small indels, structural variants such as deletions, duplications, inversions and translocations involve segments of DNAs and are potentially more accurate for allele fraction estimations. However, no systematic method exists that can support such analysis. |
X Demographics
The data shown below were collected from the profiles of 4 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 | 1 | 25% |
| Germany | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Korea, Republic of | 1 | 2% |
| United Kingdom | 1 | 2% |
| United States | 1 | 2% |
| Belgium | 1 | 2% |
| Unknown | 49 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 28% |
| Researcher | 14 | 26% |
| Student > Master | 7 | 13% |
| Student > Bachelor | 4 | 8% |
| Other | 3 | 6% |
| Other | 6 | 11% |
| Unknown | 4 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 26 | 49% |
| Computer Science | 9 | 17% |
| Biochemistry, Genetics and Molecular Biology | 5 | 9% |
| Medicine and Dentistry | 3 | 6% |
| Neuroscience | 2 | 4% |
| Other | 3 | 6% |
| Unknown | 5 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 11 September 2014.
All research outputs
#13,919,373
of 22,763,032 outputs
Outputs from BMC Bioinformatics
#4,471
of 7,273 outputs
Outputs of similar age
#119,178
of 238,613 outputs
Outputs of similar age from BMC Bioinformatics
#72
of 116 outputs
Altmetric has tracked 22,763,032 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 35th percentile – i.e., 35% 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 238,613 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 116 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.