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Mendeley readers
Attention Score in Context
| Title |
Mining biological information from 3D short time-series gene expression data: the OPTricluster algorithm
|
|---|---|
| Published in |
BMC Bioinformatics, April 2012
|
| DOI | 10.1186/1471-2105-13-54 |
| Pubmed ID | |
| Authors |
Alain B Tchagang, Sieu Phan, Fazel Famili, Heather Shearer, Pierre Fobert, Yi Huang, Jitao Zou, Daiqing Huang, Adrian Cutler, Ziying Liu, Youlian Pan |
| Abstract |
Nowadays, it is possible to collect expression levels of a set of genes from a set of biological samples during a series of time points. Such data have three dimensions: gene-sample-time (GST). Thus they are called 3D microarray gene expression data. To take advantage of the 3D data collected, and to fully understand the biological knowledge hidden in the GST data, novel subspace clustering algorithms have to be developed to effectively address the biological problem in the corresponding space. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 6% |
| Germany | 1 | 2% |
| Portugal | 1 | 2% |
| United Kingdom | 1 | 2% |
| Indonesia | 1 | 2% |
| Unknown | 42 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 33% |
| Researcher | 13 | 27% |
| Student > Master | 4 | 8% |
| Professor > Associate Professor | 3 | 6% |
| Student > Postgraduate | 2 | 4% |
| Other | 5 | 10% |
| Unknown | 6 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 15 | 31% |
| Agricultural and Biological Sciences | 9 | 18% |
| Biochemistry, Genetics and Molecular Biology | 7 | 14% |
| Mathematics | 3 | 6% |
| Medicine and Dentistry | 2 | 4% |
| Other | 4 | 8% |
| Unknown | 9 | 18% |
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 17 April 2012.
All research outputs
#14,725,504
of 22,664,267 outputs
Outputs from BMC Bioinformatics
#5,033
of 7,247 outputs
Outputs of similar age
#99,427
of 161,215 outputs
Outputs of similar age from BMC Bioinformatics
#61
of 90 outputs
Altmetric has tracked 22,664,267 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 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 26th percentile – i.e., 26% 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 161,215 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 90 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.