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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Genecentric: a package to uncover graph-theoretic structure in high-throughput epistasis data
|
|---|---|
| Published in |
BMC Bioinformatics, January 2013
|
| DOI | 10.1186/1471-2105-14-23 |
| Pubmed ID | |
| Authors |
Andrew Gallant, Mark DM Leiserson, Maxim Kachalov, Lenore J Cowen, Benjamin J Hescott |
| Abstract |
New technology has resulted in high-throughput screens for pairwise genetic interactions in yeast and other model organisms. For each pair in a collection of non-essential genes, an epistasis score is obtained, representing how much sicker (or healthier) the double-knockout organism will be compared to what would be expected from the sickness of the component single knockouts. Recent algorithmic work has identified graph-theoretic patterns in this data that can indicate functional modules, and even sets of genes that may occur in compensatory pathways, such as a BPM-type schema first introduced by Kelley and Ideker. However, to date, any algorithms for finding such patterns in the data were implemented internally, with no software being made publically available. |
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 States | 1 | 33% |
| Norway | 1 | 33% |
| Brazil | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 8% |
| Italy | 1 | 8% |
| Unknown | 11 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 38% |
| Researcher | 4 | 31% |
| Student > Doctoral Student | 1 | 8% |
| Student > Master | 1 | 8% |
| Unknown | 2 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 38% |
| Agricultural and Biological Sciences | 3 | 23% |
| Business, Management and Accounting | 1 | 8% |
| Biochemistry, Genetics and Molecular Biology | 1 | 8% |
| Unknown | 3 | 23% |
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 22 January 2013.
All research outputs
#14,098,018
of 22,693,205 outputs
Outputs from BMC Bioinformatics
#4,700
of 7,254 outputs
Outputs of similar age
#168,863
of 284,846 outputs
Outputs of similar age from BMC Bioinformatics
#84
of 139 outputs
Altmetric has tracked 22,693,205 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 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 35th percentile – i.e., 35% 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 284,846 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 139 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.