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Mendeley readers
Attention Score in Context
Title |
An inferential framework for biological network hypothesis tests
|
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Published in |
BMC Bioinformatics, March 2013
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DOI | 10.1186/1471-2105-14-94 |
Pubmed ID | |
Authors |
Phillip D Yates, Nitai D Mukhopadhyay |
Abstract |
Networks are ubiquitous in modern cell biology and physiology. A large literature exists for inferring/proposing biological pathways/networks using statistical or machine learning algorithms. Despite these advances a formal testing procedure for analyzing network-level observations is in need of further development. Comparing the behaviour of a pharmacologically altered pathway to its canonical form is an example of a salient one-sample comparison. Locating which pathways differentiate disease from no-disease phenotype may be recast as a two-sample network inference problem. |
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% |
Unknown | 2 | 67% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 3% |
United Kingdom | 2 | 2% |
Italy | 1 | 1% |
Brazil | 1 | 1% |
France | 1 | 1% |
Sweden | 1 | 1% |
Belgium | 1 | 1% |
Luxembourg | 1 | 1% |
Unknown | 76 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 26 | 30% |
Researcher | 25 | 29% |
Student > Master | 9 | 10% |
Professor | 7 | 8% |
Student > Bachelor | 4 | 5% |
Other | 9 | 10% |
Unknown | 7 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 26 | 30% |
Biochemistry, Genetics and Molecular Biology | 13 | 15% |
Computer Science | 9 | 10% |
Medicine and Dentistry | 8 | 9% |
Mathematics | 6 | 7% |
Other | 13 | 15% |
Unknown | 12 | 14% |
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 29 March 2013.
All research outputs
#13,380,136
of 22,701,287 outputs
Outputs from BMC Bioinformatics
#4,190
of 7,254 outputs
Outputs of similar age
#105,195
of 196,101 outputs
Outputs of similar age from BMC Bioinformatics
#89
of 146 outputs
Altmetric has tracked 22,701,287 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% 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 38th percentile – i.e., 38% 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 196,101 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.