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Mendeley readers
Attention Score in Context
| Title |
Quality assessment of tandem mass spectra using support vector machine (SVM)
|
|---|---|
| Published in |
BMC Bioinformatics, January 2009
|
| DOI | 10.1186/1471-2105-10-s1-s49 |
| Pubmed ID | |
| Authors |
An-Min Zou, Fang-Xiang Wu, Jia-Rui Ding, Guy G Poirier |
| Abstract |
Tandem mass spectrometry has become particularly useful for the rapid identification and characterization of protein components of complex biological mixtures. Powerful database search methods have been developed for the peptide identification, such as SEQUEST and MASCOT, which are implemented by comparing the mass spectra obtained from unknown proteins or peptides with theoretically predicted spectra derived from protein databases. However, the majority of spectra generated from a mass spectrometry experiment are of too poor quality to be interpreted while some of spectra with high quality cannot be interpreted by one method but perhaps by others. Hence a filtering algorithm that removes those spectra with poor quality prior to the database search is appealing. |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 4% |
| China | 1 | 4% |
| Germany | 1 | 4% |
| Unknown | 20 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 30% |
| Researcher | 5 | 22% |
| Student > Master | 2 | 9% |
| Student > Doctoral Student | 1 | 4% |
| Student > Bachelor | 1 | 4% |
| Other | 4 | 17% |
| Unknown | 3 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 39% |
| Computer Science | 4 | 17% |
| Chemistry | 3 | 13% |
| Biochemistry, Genetics and Molecular Biology | 1 | 4% |
| Unspecified | 1 | 4% |
| Other | 3 | 13% |
| Unknown | 2 | 9% |
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 03 August 2011.
All research outputs
#15,236,094
of 22,653,392 outputs
Outputs from BMC Bioinformatics
#5,355
of 7,236 outputs
Outputs of similar age
#142,681
of 170,071 outputs
Outputs of similar age from BMC Bioinformatics
#47
of 58 outputs
Altmetric has tracked 22,653,392 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% 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 18th percentile – i.e., 18% 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 170,071 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.