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X Demographics
Mendeley readers
Attention Score in Context
| Title |
attract: A Method for Identifying Core Pathways That Define Cellular Phenotypes
|
|---|---|
| Published in |
PLOS ONE, October 2011
|
| DOI | 10.1371/journal.pone.0025445 |
| Pubmed ID | |
| Authors |
Jessica C. Mar, Nicholas A. Matigian, John Quackenbush, Christine A. Wells |
| Abstract |
attract is a knowledge-driven analytical approach for identifying and annotating the gene-sets that best discriminate between cell phenotypes. attract finds distinguishing patterns within pathways, decomposes pathways into meta-genes representative of these patterns, and then generates synexpression groups of highly correlated genes from the entire transcriptome dataset. attract can be applied to a wide range of biological systems and is freely available as a Bioconductor package and has been incorporated into the MeV software system. |
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 % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 7% |
| Portugal | 1 | 1% |
| Germany | 1 | 1% |
| Hong Kong | 1 | 1% |
| Sweden | 1 | 1% |
| United Kingdom | 1 | 1% |
| France | 1 | 1% |
| Argentina | 1 | 1% |
| Singapore | 1 | 1% |
| Other | 2 | 3% |
| Unknown | 61 | 80% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 24 | 32% |
| Researcher | 21 | 28% |
| Professor > Associate Professor | 7 | 9% |
| Professor | 6 | 8% |
| Other | 5 | 7% |
| Other | 9 | 12% |
| Unknown | 4 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 42 | 55% |
| Biochemistry, Genetics and Molecular Biology | 10 | 13% |
| Engineering | 7 | 9% |
| Mathematics | 3 | 4% |
| Computer Science | 3 | 4% |
| Other | 7 | 9% |
| Unknown | 4 | 5% |
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 19 October 2011.
All research outputs
#17,625,645
of 22,653,392 outputs
Outputs from PLOS ONE
#146,018
of 193,422 outputs
Outputs of similar age
#111,075
of 136,361 outputs
Outputs of similar age from PLOS ONE
#2,055
of 2,564 outputs
Altmetric has tracked 22,653,392 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 193,422 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one is in the 24th percentile – i.e., 24% 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 136,361 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2,564 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.