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Mendeley readers
Attention Score in Context
| Title |
A statistical framework for differential network analysis from microarray data
|
|---|---|
| Published in |
BMC Bioinformatics, February 2010
|
| DOI | 10.1186/1471-2105-11-95 |
| Pubmed ID | |
| Authors |
Ryan Gill, Somnath Datta, Susmita Datta |
| Abstract |
It has been long well known that genes do not act alone; rather groups of genes act in consort during a biological process. Consequently, the expression levels of genes are dependent on each other. Experimental techniques to detect such interacting pairs of genes have been in place for quite some time. With the advent of microarray technology, newer computational techniques to detect such interaction or association between gene expressions are being proposed which lead to an association network. While most microarray analyses look for genes that are differentially expressed, it is of potentially greater significance to identify how entire association network structures change between two or more biological settings, say normal versus diseased cell types. |
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 | 3 | 2% |
| Spain | 3 | 2% |
| Sweden | 2 | 1% |
| Luxembourg | 2 | 1% |
| Canada | 2 | 1% |
| South Africa | 1 | <1% |
| Finland | 1 | <1% |
| Brazil | 1 | <1% |
| France | 1 | <1% |
| Other | 7 | 4% |
| Unknown | 159 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 49 | 27% |
| Researcher | 42 | 23% |
| Professor > Associate Professor | 18 | 10% |
| Student > Master | 15 | 8% |
| Professor | 11 | 6% |
| Other | 33 | 18% |
| Unknown | 14 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 72 | 40% |
| Biochemistry, Genetics and Molecular Biology | 22 | 12% |
| Computer Science | 21 | 12% |
| Mathematics | 17 | 9% |
| Engineering | 10 | 5% |
| Other | 25 | 14% |
| Unknown | 15 | 8% |
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 23 March 2014.
All research outputs
#20,224,618
of 22,749,166 outputs
Outputs from BMC Bioinformatics
#6,840
of 7,268 outputs
Outputs of similar age
#89,771
of 94,036 outputs
Outputs of similar age from BMC Bioinformatics
#64
of 66 outputs
Altmetric has tracked 22,749,166 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,268 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% 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 94,036 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 66 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.