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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Identification of properties important to protein aggregation using feature selection
|
|---|---|
| Published in |
BMC Bioinformatics, October 2013
|
| DOI | 10.1186/1471-2105-14-314 |
| Pubmed ID | |
| Authors |
Yaping Fang, Shan Gao, David Tai, C Russell Middaugh, Jianwen Fang |
| Abstract |
Protein aggregation is a significant problem in the biopharmaceutical industry (protein drug stability) and is associated medically with over 40 human diseases. Although a number of computational models have been developed for predicting aggregation propensity and identifying aggregation-prone regions in proteins, little systematic research has been done to determine physicochemical properties relevant to aggregation and their relative importance to this important process. Such studies may result in not only accurately predicting peptide aggregation propensities and identifying aggregation prone regions in proteins, but also aid in discovering additional underlying mechanisms governing this process. |
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 % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 2% |
| United States | 1 | 1% |
| Belgium | 1 | 1% |
| Switzerland | 1 | 1% |
| Unknown | 93 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 24 | 24% |
| Student > Ph. D. Student | 22 | 22% |
| Student > Master | 16 | 16% |
| Student > Bachelor | 9 | 9% |
| Student > Doctoral Student | 3 | 3% |
| Other | 10 | 10% |
| Unknown | 14 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 37 | 38% |
| Biochemistry, Genetics and Molecular Biology | 16 | 16% |
| Pharmacology, Toxicology and Pharmaceutical Science | 7 | 7% |
| Chemistry | 6 | 6% |
| Engineering | 5 | 5% |
| Other | 12 | 12% |
| Unknown | 15 | 15% |
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 28 October 2013.
All research outputs
#18,351,676
of 22,727,570 outputs
Outputs from BMC Bioinformatics
#6,300
of 7,266 outputs
Outputs of similar age
#158,166
of 212,687 outputs
Outputs of similar age from BMC Bioinformatics
#93
of 115 outputs
Altmetric has tracked 22,727,570 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 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 5th percentile – i.e., 5% 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 212,687 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 115 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.