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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Identifying cancer biomarkers by network-constrained support vector machines
|
|---|---|
| Published in |
BMC Systems Biology, October 2011
|
| DOI | 10.1186/1752-0509-5-161 |
| Pubmed ID | |
| Authors |
Li Chen, Jianhua Xuan, Rebecca B Riggins, Robert Clarke, Yue Wang |
| Abstract |
One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers. |
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 % |
|---|---|---|
| Unknown | 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 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 5% |
| Mexico | 1 | <1% |
| Netherlands | 1 | <1% |
| China | 1 | <1% |
| Unknown | 143 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 43 | 28% |
| Researcher | 28 | 18% |
| Student > Master | 12 | 8% |
| Student > Doctoral Student | 11 | 7% |
| Student > Bachelor | 10 | 7% |
| Other | 24 | 16% |
| Unknown | 25 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 50 | 33% |
| Computer Science | 24 | 16% |
| Biochemistry, Genetics and Molecular Biology | 19 | 12% |
| Medicine and Dentistry | 11 | 7% |
| Mathematics | 5 | 3% |
| Other | 11 | 7% |
| Unknown | 33 | 22% |
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 22 January 2015.
All research outputs
#14,720,232
of 22,655,397 outputs
Outputs from BMC Systems Biology
#601
of 1,142 outputs
Outputs of similar age
#91,922
of 135,954 outputs
Outputs of similar age from BMC Systems Biology
#20
of 41 outputs
Altmetric has tracked 22,655,397 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 43rd percentile – i.e., 43% 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 135,954 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.