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Mendeley readers
Attention Score in Context
Title |
More powerful significant testing for time course gene expression data using functional principal component analysis approaches
|
---|---|
Published in |
BMC Bioinformatics, January 2013
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DOI | 10.1186/1471-2105-14-6 |
Pubmed ID | |
Authors |
Shuang Wu, Hulin Wu |
Abstract |
One of the fundamental problems in time course gene expression data analysis is to identify genes associated with a biological process or a particular stimulus of interest, like a treatment or virus infection. Most of the existing methods for this problem are designed for data with longitudinal replicates. But in reality, many time course gene experiments have no replicates or only have a small number of independent replicates. |
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 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Japan | 1 | 1% |
United Kingdom | 1 | 1% |
United States | 1 | 1% |
Unknown | 76 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 20 | 25% |
Researcher | 19 | 24% |
Student > Postgraduate | 8 | 10% |
Student > Master | 7 | 9% |
Professor > Associate Professor | 6 | 8% |
Other | 9 | 11% |
Unknown | 10 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 30 | 38% |
Biochemistry, Genetics and Molecular Biology | 11 | 14% |
Mathematics | 6 | 8% |
Computer Science | 6 | 8% |
Medicine and Dentistry | 4 | 5% |
Other | 9 | 11% |
Unknown | 13 | 16% |
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 30 September 2013.
All research outputs
#18,348,542
of 22,723,682 outputs
Outputs from BMC Bioinformatics
#6,295
of 7,262 outputs
Outputs of similar age
#221,525
of 285,052 outputs
Outputs of similar age from BMC Bioinformatics
#110
of 139 outputs
Altmetric has tracked 22,723,682 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,262 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 5th percentile – i.e., 5% 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 285,052 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% 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 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.