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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A flexible statistical model for alignment of label-free proteomics data - incorporating ion mobility and product ion information
|
|---|---|
| Published in |
BMC Bioinformatics, December 2013
|
| DOI | 10.1186/1471-2105-14-364 |
| Pubmed ID | |
| Authors |
Ashlee M Benjamin, J Will Thompson, Erik J Soderblom, Scott J Geromanos, Ricardo Henao, Virginia B Kraus, M Arthur Moseley, Joseph E Lucas |
| Abstract |
The goal of many proteomics experiments is to determine the abundance of proteins in biological samples, and the variation thereof in various physiological conditions. High-throughput quantitative proteomics, specifically label-free LC-MS/MS, allows rapid measurement of thousands of proteins, enabling large-scale studies of various biological systems. Prior to analyzing these information-rich datasets, raw data must undergo several computational processing steps. We present a method to address one of the essential steps in proteomics data processing--the matching of peptide measurements across samples. |
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 % |
|---|---|---|
| Spain | 1 | 50% |
| Norway | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Netherlands | 1 | 3% |
| Colombia | 1 | 3% |
| South Africa | 1 | 3% |
| Unknown | 32 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 28% |
| Student > Ph. D. Student | 7 | 19% |
| Student > Master | 5 | 14% |
| Other | 2 | 6% |
| Professor | 2 | 6% |
| Other | 4 | 11% |
| Unknown | 6 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 10 | 28% |
| Computer Science | 3 | 8% |
| Chemistry | 3 | 8% |
| Biochemistry, Genetics and Molecular Biology | 2 | 6% |
| Medicine and Dentistry | 2 | 6% |
| Other | 9 | 25% |
| Unknown | 7 | 19% |
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 16 December 2013.
All research outputs
#17,706,524
of 22,736,112 outputs
Outputs from BMC Bioinformatics
#5,924
of 7,266 outputs
Outputs of similar age
#223,336
of 307,527 outputs
Outputs of similar age from BMC Bioinformatics
#81
of 106 outputs
Altmetric has tracked 22,736,112 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 7,266 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 307,527 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 106 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.