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X Demographics
Mendeley readers
Attention Score in Context
| Title |
SIGNATURE: A workbench for gene expression signature analysis
|
|---|---|
| Published in |
BMC Bioinformatics, November 2011
|
| DOI | 10.1186/1471-2105-12-443 |
| Pubmed ID | |
| Authors |
Jeffrey T Chang, Michael L Gatza, Joseph E Lucas, William T Barry, Peyton Vaughn, Joseph R Nevins |
| Abstract |
The biological phenotype of a cell, such as a characteristic visual image or behavior, reflects activities derived from the expression of collections of genes. As such, an ability to measure the expression of these genes provides an opportunity to develop more precise and varied sets of phenotypes. However, to use this approach requires computational methods that are difficult to implement and apply, and thus there is a critical need for intelligent software tools that can reduce the technical burden of the analysis. Tools for gene expression analyses are unusually difficult to implement in a user-friendly way because their application requires a combination of biological data curation, statistical computational methods, and database expertise. |
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 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 4% |
| Israel | 1 | 1% |
| Germany | 1 | 1% |
| Unknown | 79 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 27 | 32% |
| Student > Ph. D. Student | 11 | 13% |
| Student > Master | 8 | 10% |
| Student > Bachelor | 6 | 7% |
| Other | 6 | 7% |
| Other | 17 | 20% |
| Unknown | 9 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 34 | 40% |
| Biochemistry, Genetics and Molecular Biology | 15 | 18% |
| Medicine and Dentistry | 9 | 11% |
| Computer Science | 6 | 7% |
| Engineering | 3 | 4% |
| Other | 7 | 8% |
| Unknown | 10 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 38. 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 31 January 2017.
All research outputs
#902,542
of 22,656,971 outputs
Outputs from BMC Bioinformatics
#77
of 7,236 outputs
Outputs of similar age
#3,947
of 141,521 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 118 outputs
Altmetric has tracked 22,656,971 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,236 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 98% 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 141,521 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 118 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.