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Mendeley readers
Attention Score in Context
| Title |
A scalable method for discovering significant subnetworks
|
|---|---|
| Published in |
BMC Systems Biology, October 2013
|
| DOI | 10.1186/1752-0509-7-s4-s3 |
| Pubmed ID | |
| Authors |
Md Mahmudul Hasan, Yusuf Kavurucu, Tamer Kahveci |
| Abstract |
Study of biological networks is an essential first step to understand the complex functions they govern in different organisms. The topology of interactions that define how biological networks operate is often determined through high-throughput experiments. Noisy nature of high-throughput experiments, however, can result in multiple alternative network topologies that explain this data equally well. One key step to resolve the differences is to identify the subnetworks which appear significantly more frequently in a biological network data set than expected. |
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 % |
|---|---|---|
| United States | 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 13% |
| Unknown | 14 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 44% |
| Researcher | 5 | 31% |
| Student > Bachelor | 2 | 13% |
| Other | 1 | 6% |
| Unknown | 1 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 31% |
| Agricultural and Biological Sciences | 4 | 25% |
| Biochemistry, Genetics and Molecular Biology | 3 | 19% |
| Arts and Humanities | 2 | 13% |
| Neuroscience | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 1 | 6% |
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 08 November 2014.
All research outputs
#17,286,379
of 25,374,917 outputs
Outputs from BMC Systems Biology
#651
of 1,132 outputs
Outputs of similar age
#140,835
of 224,696 outputs
Outputs of similar age from BMC Systems Biology
#20
of 35 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 31st percentile – i.e., 31% 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 224,696 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.