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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Improving the sensitivity of sample clustering by leveraging gene co-expression networks in variable selection
|
|---|---|
| Published in |
BMC Bioinformatics, May 2014
|
| DOI | 10.1186/1471-2105-15-153 |
| Pubmed ID | |
| Authors |
Zixing Wang, F Anthony San Lucas, Peng Qiu, Yin Liu |
| Abstract |
Many variable selection techniques have been proposed for the clustering of gene expression data. While these methods tend to filter out irrelevant genes and identify informative genes that contribute to a clustering solution, they are based on criteria that do not consider the potential interactive influence among individual genes. Motivated by ensemble clustering, there is a strong interest in leveraging the structure of gene networks for gene selection, so that the relationship information between genes can be effectively utilized, while the selected genes are expected to preserve all the possible clustering structures in the data. |
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 % |
|---|---|---|
| Norway | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 4% |
| Brazil | 1 | 4% |
| Unknown | 22 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 38% |
| Student > Ph. D. Student | 7 | 29% |
| Student > Bachelor | 2 | 8% |
| Professor | 1 | 4% |
| Student > Doctoral Student | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 3 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 33% |
| Computer Science | 4 | 17% |
| Biochemistry, Genetics and Molecular Biology | 2 | 8% |
| Mathematics | 2 | 8% |
| Medicine and Dentistry | 2 | 8% |
| Other | 2 | 8% |
| Unknown | 4 | 17% |
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 21 May 2014.
All research outputs
#18,372,841
of 22,756,196 outputs
Outputs from BMC Bioinformatics
#6,303
of 7,271 outputs
Outputs of similar age
#162,999
of 226,286 outputs
Outputs of similar age from BMC Bioinformatics
#115
of 150 outputs
Altmetric has tracked 22,756,196 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 7,271 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 226,286 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 150 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.