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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A method to identify differential expression profiles of time-course gene data with Fourier transformation
|
|---|---|
| Published in |
BMC Bioinformatics, October 2013
|
| DOI | 10.1186/1471-2105-14-310 |
| Pubmed ID | |
| Authors |
Jaehee Kim, Robert Todd Ogden, Haseong Kim |
| Abstract |
Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis. |
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 % |
|---|---|---|
| Mexico | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| India | 1 | 2% |
| Unknown | 63 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 24 | 37% |
| Student > Ph. D. Student | 20 | 31% |
| Professor > Associate Professor | 4 | 6% |
| Student > Master | 4 | 6% |
| Professor | 3 | 5% |
| Other | 7 | 11% |
| Unknown | 3 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 34 | 52% |
| Biochemistry, Genetics and Molecular Biology | 7 | 11% |
| Computer Science | 6 | 9% |
| Engineering | 5 | 8% |
| Psychology | 2 | 3% |
| Other | 6 | 9% |
| Unknown | 5 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 23 October 2013.
All research outputs
#12,885,552
of 22,727,570 outputs
Outputs from BMC Bioinformatics
#3,786
of 7,262 outputs
Outputs of similar age
#106,897
of 211,746 outputs
Outputs of similar age from BMC Bioinformatics
#50
of 108 outputs
Altmetric has tracked 22,727,570 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% 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 45th percentile – i.e., 45% 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 211,746 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.