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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Exploiting the noise: improving biomarkers with ensembles of data analysis methodologies
|
|---|---|
| Published in |
Genome Medicine, November 2012
|
| DOI | 10.1186/gm385 |
| Pubmed ID | |
| Authors |
Maud HW Starmans, Melania Pintilie, Thomas John, Sandy D Der, Frances A Shepherd, Igor Jurisica, Philippe Lambin, Ming-Sound Tsao, Paul C Boutros |
| Abstract |
The advent of personalized medicine requires robust, reproducible biomarkers that indicate which treatment will maximize therapeutic benefit while minimizing side effects and costs. Numerous molecular signatures have been developed over the past decade to fill this need, but their validation and up-take into clinical settings has been poor. Here, we investigate the technical reasons underlying reported failures in biomarker validation for non-small cell lung cancer (NSCLC). |
X Demographics
The data shown below were collected from the profiles of 7 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 Kingdom | 3 | 43% |
| Peru | 1 | 14% |
| Unknown | 3 | 43% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 43% |
| Science communicators (journalists, bloggers, editors) | 2 | 29% |
| Scientists | 2 | 29% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 2 | 4% |
| United States | 1 | 2% |
| Unknown | 48 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 37% |
| Student > Master | 6 | 12% |
| Student > Bachelor | 5 | 10% |
| Student > Doctoral Student | 4 | 8% |
| Professor | 4 | 8% |
| Other | 9 | 18% |
| Unknown | 4 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 18 | 35% |
| Medicine and Dentistry | 14 | 27% |
| Biochemistry, Genetics and Molecular Biology | 7 | 14% |
| Computer Science | 2 | 4% |
| Mathematics | 2 | 4% |
| Other | 2 | 4% |
| Unknown | 6 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 November 2012.
All research outputs
#3,016,563
of 25,374,647 outputs
Outputs from Genome Medicine
#684
of 1,585 outputs
Outputs of similar age
#21,188
of 193,286 outputs
Outputs of similar age from Genome Medicine
#11
of 26 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,585 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.8. This one has gotten more attention than average, scoring higher than 56% of its peers.
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 193,286 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.