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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Better score function for peptide identification with ETD MS/MS spectra
|
|---|---|
| Published in |
BMC Bioinformatics, January 2010
|
| DOI | 10.1186/1471-2105-11-s1-s4 |
| Pubmed ID | |
| Authors |
Xiaowen Liu, Baozhen Shan, Lei Xin, Bin Ma |
| Abstract |
Tandem mass spectrometry (MS/MS) has become the primary way for protein identification in proteomics. A good score function for measuring the match quality between a peptide and an MS/MS spectrum is instrumental for the protein identification. Traditionally the to-be-measured peptides are fragmented with the collision induced dissociation (CID) method. More recently, the electron transfer dissociation (ETD) method was introduced and has proven to produce better fragment ion ladders for larger and more basic peptides. However, the existing software programs that analyze ETD MS/MS data are not as advanced as they are for CID. |
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 % |
|---|---|---|
| Egypt | 1 | 50% |
| United Kingdom | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 3% |
| Brazil | 1 | 3% |
| Canada | 1 | 3% |
| China | 1 | 3% |
| United States | 1 | 3% |
| Unknown | 26 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 39% |
| Researcher | 7 | 23% |
| Student > Master | 4 | 13% |
| Professor | 2 | 6% |
| Student > Doctoral Student | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 2 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 10 | 32% |
| Computer Science | 9 | 29% |
| Chemistry | 5 | 16% |
| Biochemistry, Genetics and Molecular Biology | 1 | 3% |
| Unspecified | 1 | 3% |
| Other | 4 | 13% |
| Unknown | 1 | 3% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 13 October 2012.
All research outputs
#6,905,877
of 22,649,029 outputs
Outputs from BMC Bioinformatics
#2,684
of 7,234 outputs
Outputs of similar age
#45,597
of 163,766 outputs
Outputs of similar age from BMC Bioinformatics
#13
of 58 outputs
Altmetric has tracked 22,649,029 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 7,234 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 61% 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 163,766 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
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 has done well, scoring higher than 75% of its contemporaries.