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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Computational Methods in Mass Spectrometry-Based Proteomics.
|
|---|---|
| Chapter number | 4 |
| Book title |
Translational Biomedical Informatics
|
| Published in |
Advances in experimental medicine and biology, November 2016
|
| DOI | 10.1007/978-981-10-1503-8_4 |
| Pubmed ID | |
| Book ISBNs |
978-9-81-101502-1, 978-9-81-101503-8
|
| Authors |
Sujun Li, Haixu Tang |
| Editors |
Bairong Shen, Haixu Tang, Xiaoqian Jiang |
| Abstract |
This chapter introduces computational methods used in mass spectrometry-based proteomics, including those for addressing the critical problems such as peptide identification and protein inference, peptide and protein quantification, characterization of posttranslational modifications (PTMs), and data-independent acquisitions (DIA). The chapter concludes with emerging applications of proteomic techniques, such as metaproteomics, glycoproteomics, and proteogenomics. |
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 % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 32 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 25% |
| Researcher | 3 | 9% |
| Student > Bachelor | 3 | 9% |
| Unspecified | 2 | 6% |
| Professor | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 12 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 19% |
| Chemistry | 4 | 13% |
| Unspecified | 2 | 6% |
| Agricultural and Biological Sciences | 2 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 6% |
| Other | 4 | 13% |
| Unknown | 12 | 38% |
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 27 June 2017.
All research outputs
#17,825,154
of 22,899,952 outputs
Outputs from Advances in experimental medicine and biology
#3,102
of 4,953 outputs
Outputs of similar age
#222,027
of 311,569 outputs
Outputs of similar age from Advances in experimental medicine and biology
#55
of 86 outputs
Altmetric has tracked 22,899,952 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 4,953 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 33rd percentile – i.e., 33% 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 311,569 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 86 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.