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Mendeley readers
Attention Score in Context
| Title |
ICGE: an R package for detecting relevant clusters and atypical units in gene expression
|
|---|---|
| Published in |
BMC Bioinformatics, February 2012
|
| DOI | 10.1186/1471-2105-13-30 |
| Pubmed ID | |
| Authors |
Itziar Irigoien, Basilio Sierra, Concepcion Arenas |
| Abstract |
Gene expression technologies have opened up new ways to diagnose and treat cancer and other diseases. Clustering algorithms are a useful approach with which to analyze genome expression data. They attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. An important problem associated with gene classification is to discern whether the clustering process can find a relevant partition as well as the identification of new genes classes. There are two key aspects to classification: the estimation of the number of clusters, and the decision as to whether a new unit (gene, tumor sample...) belongs to one of these previously identified clusters or to a new group. |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 3% |
| Colombia | 1 | 3% |
| United States | 1 | 3% |
| Norway | 1 | 3% |
| Unknown | 33 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 32% |
| Student > Ph. D. Student | 9 | 24% |
| Professor | 4 | 11% |
| Student > Doctoral Student | 2 | 5% |
| Student > Bachelor | 2 | 5% |
| Other | 6 | 16% |
| Unknown | 2 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 46% |
| Computer Science | 5 | 14% |
| Biochemistry, Genetics and Molecular Biology | 4 | 11% |
| Medicine and Dentistry | 3 | 8% |
| Mathematics | 2 | 5% |
| Other | 3 | 8% |
| Unknown | 3 | 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 11 August 2020.
All research outputs
#7,668,611
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#3,077
of 7,387 outputs
Outputs of similar age
#74,076
of 252,978 outputs
Outputs of similar age from BMC Bioinformatics
#33
of 71 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,387 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 50% of its peers.
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 252,978 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 71 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.