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Mendeley readers
Attention Score in Context
| Chapter title |
Analysis of glycoproteins for biomarker discovery.
|
|---|---|
| Chapter number | 10 |
| Book title |
Proteomics for Biomarker Discovery
|
| Published in |
Methods in molecular biology, May 2013
|
| DOI | 10.1007/978-1-62703-360-2_10 |
| Pubmed ID | |
| Book ISBNs |
978-1-62703-359-6, 978-1-62703-360-2
|
| Authors |
He J, Liu Y, Wu J, Lubman DM, Jintang He, Yashu Liu, Jing Wu, David M. Lubman, He, Jintang, Liu, Yashu, Wu, Jing, Lubman, David M. |
| Abstract |
Glycoproteins play an important role in cell signaling and cell-cell interaction. The alterations of glycoproteins are often relevant to progression of diseases, and these changed glycoproteins can be important biomarkers. The lectin-based glycoproteomic technology has extensively been used for high-throughput screening of potential glycoprotein biomarkers. Here we describe a multi-lectin affinity chromatography and label-free quantitative glycoproteomic approach for discovery of glycoprotein biomarkers relevant to differentiation of glioblastoma stem cells. |
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 % |
|---|---|---|
| Hungary | 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 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 21 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 24% |
| Student > Bachelor | 2 | 10% |
| Unspecified | 2 | 10% |
| Student > Master | 2 | 10% |
| Professor > Associate Professor | 2 | 10% |
| Other | 4 | 19% |
| Unknown | 4 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 4 | 19% |
| Agricultural and Biological Sciences | 4 | 19% |
| Unspecified | 2 | 10% |
| Biochemistry, Genetics and Molecular Biology | 1 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 5% |
| Other | 4 | 19% |
| Unknown | 5 | 24% |
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 June 2013.
All research outputs
#15,272,977
of 22,711,645 outputs
Outputs from Methods in molecular biology
#5,302
of 13,079 outputs
Outputs of similar age
#119,343
of 192,821 outputs
Outputs of similar age from Methods in molecular biology
#13
of 30 outputs
Altmetric has tracked 22,711,645 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,079 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 45th percentile – i.e., 45% 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 192,821 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.