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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Characterization of uncertainty in the classification of multivariate assays: application to PAM50 centroid-based genomic predictors for breast cancer treatment plans
|
|---|---|
| Published in |
Journal of Clinical Bioinformatics, December 2011
|
| DOI | 10.1186/2043-9113-1-37 |
| Pubmed ID | |
| Authors |
Mark TW Ebbert, Roy RL Bastien, Kenneth M Boucher, Miguel Martín, Eva Carrasco, Rosalía Caballero, Inge J Stijleman, Philip S Bernard, Julio C Facelli |
| Abstract |
Multivariate assays (MVAs) for assisting clinical decisions are becoming commonly available, but due to complexity, are often considered a high-risk approach. A key concern is that uncertainty on the assay's final results is not well understood. This study focuses on developing a process to characterize error introduced in the MVA's results from the intrinsic error in the laboratory process: sample preparation and measurement of the contributing factors, such as gene expression. |
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 % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 40 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 30% |
| Student > Ph. D. Student | 7 | 18% |
| Other | 5 | 13% |
| Student > Doctoral Student | 4 | 10% |
| Student > Bachelor | 2 | 5% |
| Other | 5 | 13% |
| Unknown | 5 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 8 | 20% |
| Agricultural and Biological Sciences | 8 | 20% |
| Biochemistry, Genetics and Molecular Biology | 6 | 15% |
| Engineering | 4 | 10% |
| Computer Science | 3 | 8% |
| Other | 6 | 15% |
| Unknown | 5 | 13% |
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 23 December 2011.
All research outputs
#20,656,161
of 25,374,647 outputs
Outputs from Journal of Clinical Bioinformatics
#44
of 61 outputs
Outputs of similar age
#203,253
of 248,947 outputs
Outputs of similar age from Journal of Clinical Bioinformatics
#6
of 9 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 61 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 3rd percentile – i.e., 3% 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 248,947 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.