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X Demographics
Mendeley readers
Attention Score in Context
| Title |
An efficient post-hoc integration method improving peak alignment of metabolomics data from GCxGC/TOF-MS
|
|---|---|
| Published in |
BMC Bioinformatics, April 2013
|
| DOI | 10.1186/1471-2105-14-123 |
| Pubmed ID | |
| Authors |
Jaesik Jeong, Xiang Zhang, Xue Shi, Seongho Kim, Changyu Shen |
| Abstract |
Since peak alignment in metabolomics has a huge effect on the subsequent statistical analysis, it is considered a key preprocessing step and many peak alignment methods have been developed. However, existing peak alignment methods do not produce satisfactory results. Indeed, the lack of accuracy results from the fact that peak alignment is done separately from another preprocessing step such as identification. Therefore, a post-hoc approach, which integrates both identification and alignment results, is in urgent need for the purpose of increasing the accuracy of peak alignment. |
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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 3% |
| Brazil | 1 | 3% |
| Unknown | 37 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 21% |
| Researcher | 4 | 10% |
| Student > Doctoral Student | 4 | 10% |
| Professor | 4 | 10% |
| Student > Bachelor | 4 | 10% |
| Other | 9 | 23% |
| Unknown | 6 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 23% |
| Chemistry | 7 | 18% |
| Biochemistry, Genetics and Molecular Biology | 3 | 8% |
| Computer Science | 3 | 8% |
| Nursing and Health Professions | 2 | 5% |
| Other | 8 | 21% |
| Unknown | 7 | 18% |
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 11 April 2013.
All research outputs
#17,684,990
of 22,705,019 outputs
Outputs from BMC Bioinformatics
#5,915
of 7,254 outputs
Outputs of similar age
#144,575
of 199,476 outputs
Outputs of similar age from BMC Bioinformatics
#111
of 135 outputs
Altmetric has tracked 22,705,019 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 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 13th percentile – i.e., 13% 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 199,476 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.