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Attention Score in Context
| Title |
A flexible framework for sparse simultaneous component based data integration
|
|---|---|
| Published in |
BMC Bioinformatics, November 2011
|
| DOI | 10.1186/1471-2105-12-448 |
| Pubmed ID | |
| Authors |
Katrijn Van Deun, Tom F Wilderjans, Robert A van den Berg, Anestis Antoniadis, Iven Van Mechelen |
| Abstract |
High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 3% |
| Portugal | 1 | 2% |
| Germany | 1 | 2% |
| Italy | 1 | 2% |
| Netherlands | 1 | 2% |
| China | 1 | 2% |
| United Kingdom | 1 | 2% |
| Unknown | 52 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 33% |
| Researcher | 15 | 25% |
| Other | 4 | 7% |
| Professor > Associate Professor | 4 | 7% |
| Student > Master | 2 | 3% |
| Other | 5 | 8% |
| Unknown | 10 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 28% |
| Mathematics | 8 | 13% |
| Computer Science | 5 | 8% |
| Biochemistry, Genetics and Molecular Biology | 4 | 7% |
| Psychology | 3 | 5% |
| Other | 11 | 18% |
| Unknown | 12 | 20% |
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 21 November 2011.
All research outputs
#13,357,126
of 22,656,971 outputs
Outputs from BMC Bioinformatics
#4,186
of 7,236 outputs
Outputs of similar age
#85,725
of 141,188 outputs
Outputs of similar age from BMC Bioinformatics
#69
of 121 outputs
Altmetric has tracked 22,656,971 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,236 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 38th percentile – i.e., 38% 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 141,188 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 121 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.