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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Evaluation of Gene Association Methods for Coexpression Network Construction and Biological Knowledge Discovery
|
|---|---|
| Published in |
PLOS ONE, November 2012
|
| DOI | 10.1371/journal.pone.0050411 |
| Pubmed ID | |
| Authors |
Sapna Kumari, Jeff Nie, Huann-Sheng Chen, Hao Ma, Ron Stewart, Xiang Li, Meng-Zhu Lu, William M. Taylor, Hairong Wei |
| Abstract |
Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. |
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 182 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 2% |
| Germany | 1 | <1% |
| Brazil | 1 | <1% |
| Netherlands | 1 | <1% |
| India | 1 | <1% |
| Sweden | 1 | <1% |
| Unknown | 173 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 62 | 34% |
| Researcher | 24 | 13% |
| Student > Master | 22 | 12% |
| Student > Postgraduate | 14 | 8% |
| Student > Bachelor | 11 | 6% |
| Other | 28 | 15% |
| Unknown | 21 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 66 | 36% |
| Biochemistry, Genetics and Molecular Biology | 43 | 24% |
| Computer Science | 21 | 12% |
| Engineering | 5 | 3% |
| Medicine and Dentistry | 5 | 3% |
| Other | 17 | 9% |
| Unknown | 25 | 14% |
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 February 2014.
All research outputs
#17,671,894
of 22,687,320 outputs
Outputs from PLOS ONE
#146,345
of 193,653 outputs
Outputs of similar age
#206,914
of 276,634 outputs
Outputs of similar age from PLOS ONE
#3,210
of 4,722 outputs
Altmetric has tracked 22,687,320 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 193,653 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 20th percentile – i.e., 20% 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 276,634 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4,722 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.