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 |
AucPR: An AUC-based approach using penalized regression for disease prediction with high-dimensional omics data
|
|---|---|
| Published in |
BMC Genomics, December 2014
|
| DOI | 10.1186/1471-2164-15-s10-s1 |
| Pubmed ID | |
| Authors |
Wenbao Yu, Taesung Park |
| Abstract |
It is common to get an optimal combination of markers for disease classification and prediction when multiple markers are available. Many approaches based on the area under the receiver operating characteristic curve (AUC) have been proposed. Existing works based on AUC in a high-dimensional context depend mainly on a non-parametric, smooth approximation of AUC, with no work using a parametric AUC-based approach, for high-dimensional data. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 5% |
| Belgium | 1 | 5% |
| Unknown | 19 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 29% |
| Student > Master | 3 | 14% |
| Student > Ph. D. Student | 3 | 14% |
| Student > Doctoral Student | 2 | 10% |
| Other | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 4 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 4 | 19% |
| Medicine and Dentistry | 4 | 19% |
| Computer Science | 3 | 14% |
| Biochemistry, Genetics and Molecular Biology | 2 | 10% |
| Nursing and Health Professions | 1 | 5% |
| Other | 3 | 14% |
| Unknown | 4 | 19% |
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 16 July 2015.
All research outputs
#18,388,295
of 22,776,824 outputs
Outputs from BMC Genomics
#8,171
of 10,643 outputs
Outputs of similar age
#258,279
of 356,570 outputs
Outputs of similar age from BMC Genomics
#183
of 234 outputs
Altmetric has tracked 22,776,824 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 10,643 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 12th percentile – i.e., 12% 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 356,570 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 234 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.