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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Comparison of methods to identify aberrant expression patterns in individual patients: augmenting our toolkit for precision medicine
|
|---|---|
| Published in |
Genome Medicine, November 2013
|
| DOI | 10.1186/gm509 |
| Pubmed ID | |
| Authors |
Daniel Bottomly, Peter A Ryabinin, Jeffrey W Tyner, Bill H Chang, Marc M Loriaux, Brian J Druker, Shannon K McWeeney, Beth Wilmot |
| Abstract |
Patient-specific aberrant expression patterns in conjunction with functional screening assays can guide elucidation of the cancer genome architecture and identification of therapeutic targets. Since most statistical methods for expression analysis are focused on differences between experimental groups, the performance of approaches for patient-specific expression analyses are currently less well characterized. A comparison of methods for the identification of genes that are dysregulated relative to a single sample in a given set of experimental samples, to our knowledge, has not been performed. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 2 | 67% |
| Germany | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 3% |
| Unknown | 34 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 23% |
| Student > Ph. D. Student | 7 | 20% |
| Other | 4 | 11% |
| Student > Master | 4 | 11% |
| Student > Bachelor | 2 | 6% |
| Other | 5 | 14% |
| Unknown | 5 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 40% |
| Biochemistry, Genetics and Molecular Biology | 6 | 17% |
| Mathematics | 3 | 9% |
| Medicine and Dentistry | 2 | 6% |
| Veterinary Science and Veterinary Medicine | 1 | 3% |
| Other | 4 | 11% |
| Unknown | 5 | 14% |
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 18 December 2013.
All research outputs
#16,721,717
of 25,373,627 outputs
Outputs from Genome Medicine
#1,429
of 1,585 outputs
Outputs of similar age
#198,801
of 320,855 outputs
Outputs of similar age from Genome Medicine
#20
of 23 outputs
Altmetric has tracked 25,373,627 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 1,585 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.8. This one is in the 8th percentile – i.e., 8% 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 320,855 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.