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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A unified computational model for revealing and predicting subtle subtypes of cancers
|
|---|---|
| Published in |
BMC Bioinformatics, May 2012
|
| DOI | 10.1186/1471-2105-13-70 |
| Pubmed ID | |
| Authors |
Xianwen Ren, Yong Wang, Jiguang Wang, Xiang-Sun Zhang |
| Abstract |
Gene expression profiling technologies have gradually become a community standard tool for clinical applications. For example, gene expression data has been analyzed to reveal novel disease subtypes (class discovery) and assign particular samples to well-defined classes (class prediction). In the past decade, many effective methods have been proposed for individual applications. However, there is still a pressing need for a unified framework that can reveal the complicated relationships between samples. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 2% |
| Sweden | 1 | 2% |
| South Africa | 1 | 2% |
| Singapore | 1 | 2% |
| Russia | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 42 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 38% |
| Student > Ph. D. Student | 7 | 15% |
| Other | 4 | 8% |
| Student > Master | 4 | 8% |
| Professor | 3 | 6% |
| Other | 4 | 8% |
| Unknown | 8 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 19 | 40% |
| Biochemistry, Genetics and Molecular Biology | 8 | 17% |
| Computer Science | 6 | 13% |
| Engineering | 2 | 4% |
| Medicine and Dentistry | 2 | 4% |
| Other | 3 | 6% |
| Unknown | 8 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 01 May 2012.
All research outputs
#18,305,773
of 22,664,644 outputs
Outputs from BMC Bioinformatics
#6,283
of 7,247 outputs
Outputs of similar age
#126,220
of 163,482 outputs
Outputs of similar age from BMC Bioinformatics
#86
of 104 outputs
Altmetric has tracked 22,664,644 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 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 5th percentile – i.e., 5% 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 163,482 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 104 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.