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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Applications of Peptide retention time in proteomic data analysis.
|
|---|---|
| Chapter number | 7 |
| Book title |
Urine Proteomics in Kidney Disease Biomarker Discovery
|
| Published in |
Advances in experimental medicine and biology, January 2015
|
| DOI | 10.1007/978-94-017-9523-4_7 |
| Pubmed ID | |
| Book ISBNs |
978-9-40-179522-7, 978-9-40-179523-4
|
| Authors |
Chen Shao |
| Abstract |
In proteomic studies, liquid chromatography is commonly used to separate peptide mixtures prior to mass spectrometry (MS) detection. As an independent dimension of information from the information provided by the MS, peptide retention time information has been proven to be able to aid proteomic data analysis in many aspects. So far, some popular software has offered options for this information for MS data acquisition and analysis. This chapter is a brief review of current methodologies of retention time prediction and application in proteomic analysis. |
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 % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 9 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 22% |
| Professor > Associate Professor | 2 | 22% |
| Unspecified | 1 | 11% |
| Professor | 1 | 11% |
| Other | 1 | 11% |
| Other | 2 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 3 | 33% |
| Biochemistry, Genetics and Molecular Biology | 2 | 22% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 11% |
| Unspecified | 1 | 11% |
| Engineering | 1 | 11% |
| Other | 0 | 0% |
| Unknown | 1 | 11% |
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 18 March 2015.
All research outputs
#18,381,794
of 22,768,097 outputs
Outputs from Advances in experimental medicine and biology
#3,304
of 4,929 outputs
Outputs of similar age
#255,652
of 352,897 outputs
Outputs of similar age from Advances in experimental medicine and biology
#161
of 271 outputs
Altmetric has tracked 22,768,097 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,929 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one is in the 19th percentile – i.e., 19% 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 352,897 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 271 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.