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Attention Score in Context
Title |
Multivariate curve resolution of time course microarray data
|
---|---|
Published in |
BMC Bioinformatics, July 2006
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DOI | 10.1186/1471-2105-7-343 |
Pubmed ID | |
Authors |
Peter D Wentzell, Tobias K Karakach, Sushmita Roy, M Juanita Martinez, Christopher P Allen, Margaret Werner-Washburne |
Abstract |
Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data. |
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 % |
---|---|---|
Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 4% |
Brazil | 2 | 2% |
Belgium | 2 | 2% |
Netherlands | 1 | 1% |
Argentina | 1 | 1% |
Unknown | 75 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 26 | 31% |
Researcher | 19 | 23% |
Student > Doctoral Student | 7 | 8% |
Student > Master | 6 | 7% |
Professor | 5 | 6% |
Other | 13 | 15% |
Unknown | 8 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 23 | 27% |
Chemistry | 18 | 21% |
Biochemistry, Genetics and Molecular Biology | 6 | 7% |
Computer Science | 4 | 5% |
Medicine and Dentistry | 4 | 5% |
Other | 16 | 19% |
Unknown | 13 | 15% |
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 27 October 2019.
All research outputs
#18,370,767
of 22,753,345 outputs
Outputs from BMC Bioinformatics
#6,302
of 7,269 outputs
Outputs of similar age
#61,653
of 65,832 outputs
Outputs of similar age from BMC Bioinformatics
#35
of 40 outputs
Altmetric has tracked 22,753,345 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 7,269 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 65,832 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.