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Mendeley readers
Attention Score in Context
| Title |
BATS: a Bayesian user-friendly software for Analyzing Time Series microarray experiments
|
|---|---|
| Published in |
BMC Bioinformatics, October 2008
|
| DOI | 10.1186/1471-2105-9-415 |
| Pubmed ID | |
| Authors |
Claudia Angelini, Luisa Cutillo, Daniela De Canditiis, Margherita Mutarelli, Marianna Pensky |
| Abstract |
Gene expression levels in a given cell can be influenced by different factors, namely pharmacological or medical treatments. The response to a given stimulus is usually different for different genes and may depend on time. One of the goals of modern molecular biology is the high-throughput identification of genes associated with a particular treatment or a biological process of interest. From methodological and computational point of view, analyzing high-dimensional time course microarray data requires very specific set of tools which are usually not included in standard software packages. Recently, the authors of this paper developed a fully Bayesian approach which allows one to identify differentially expressed genes in a 'one-sample' time-course microarray experiment, to rank them and to estimate their expression profiles. The method is based on explicit expressions for calculations and, hence, very computationally efficient. |
Mendeley readers
The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| China | 1 | 2% |
| France | 1 | 2% |
| Unknown | 63 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 23 | 35% |
| Student > Ph. D. Student | 16 | 24% |
| Professor | 5 | 8% |
| Student > Master | 5 | 8% |
| Student > Postgraduate | 4 | 6% |
| Other | 10 | 15% |
| Unknown | 3 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 31 | 47% |
| Medicine and Dentistry | 7 | 11% |
| Computer Science | 6 | 9% |
| Biochemistry, Genetics and Molecular Biology | 5 | 8% |
| Mathematics | 3 | 5% |
| Other | 6 | 9% |
| Unknown | 8 | 12% |
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 29 January 2013.
All research outputs
#20,180,477
of 22,694,633 outputs
Outputs from BMC Bioinformatics
#6,827
of 7,254 outputs
Outputs of similar age
#86,127
of 89,678 outputs
Outputs of similar age from BMC Bioinformatics
#39
of 40 outputs
Altmetric has tracked 22,694,633 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% 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 1st percentile – i.e., 1% 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 89,678 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% 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 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.