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Mendeley readers
Attention Score in Context
| Title |
Measuring similarities between gene expression profiles through new data transformations
|
|---|---|
| Published in |
BMC Bioinformatics, January 2007
|
| DOI | 10.1186/1471-2105-8-29 |
| Pubmed ID | |
| Authors |
Kyungpil Kim, Shibo Zhang, Keni Jiang, Li Cai, In-Beum Lee, Lewis J Feldman, Haiyan Huang |
| Abstract |
Clustering methods are widely used on gene expression data to categorize genes with similar expression profiles. Finding an appropriate (dis)similarity measure is critical to the analysis. In our study, we developed a new measure for clustering the genes when the key factor is the shape of the profile, and when the expression magnitude should also be accounted for in determining the gene relationship. This is achieved by modeling the shape and magnitude parameters separately in a gene expression profile, and then using the estimated shape and magnitude parameters to define a measure in a new feature space. |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| Portugal | 1 | 2% |
| Belgium | 1 | 2% |
| Unknown | 47 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 32% |
| Researcher | 13 | 26% |
| Professor > Associate Professor | 4 | 8% |
| Other | 3 | 6% |
| Student > Master | 3 | 6% |
| Other | 9 | 18% |
| Unknown | 2 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 42% |
| Computer Science | 9 | 18% |
| Biochemistry, Genetics and Molecular Biology | 6 | 12% |
| Mathematics | 2 | 4% |
| Medicine and Dentistry | 2 | 4% |
| Other | 7 | 14% |
| Unknown | 3 | 6% |
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 25 July 2011.
All research outputs
#7,454,951
of 22,790,780 outputs
Outputs from BMC Bioinformatics
#3,023
of 7,280 outputs
Outputs of similar age
#43,953
of 161,445 outputs
Outputs of similar age from BMC Bioinformatics
#21
of 48 outputs
Altmetric has tracked 22,790,780 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 7,280 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 161,445 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.