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Mendeley readers
Attention Score in Context
| Title |
Exploiting heterogeneous features to improve in silico prediction of peptide status – amyloidogenic or non-amyloidogenic
|
|---|---|
| Published in |
BMC Bioinformatics, November 2011
|
| DOI | 10.1186/1471-2105-12-s13-s21 |
| Pubmed ID | |
| Authors |
Smitha Sunil Kumaran Nair, NV Subba Reddy, KS Hareesha |
| Abstract |
Prediction of short stretches in protein sequences capable of forming amyloid-like fibrils is important in understanding the underlying cause of amyloid illnesses thereby aiding in the discovery of sequence-targeted anti-aggregation pharmaceuticals. Due to the constraints of experimental molecular techniques in identifying such motif segments, it is highly desirable to develop computational methods to provide better and affordable in silico predictions. |
Mendeley readers
The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 4% |
| Unknown | 23 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 21% |
| Student > Bachelor | 4 | 17% |
| Professor | 3 | 13% |
| Researcher | 3 | 13% |
| Other | 2 | 8% |
| Other | 5 | 21% |
| Unknown | 2 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 21% |
| Agricultural and Biological Sciences | 4 | 17% |
| Engineering | 3 | 13% |
| Biochemistry, Genetics and Molecular Biology | 2 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 8% |
| Other | 4 | 17% |
| Unknown | 4 | 17% |
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 14 July 2012.
All research outputs
#18,317,537
of 22,681,577 outputs
Outputs from BMC Bioinformatics
#6,281
of 7,250 outputs
Outputs of similar age
#195,563
of 239,780 outputs
Outputs of similar age from BMC Bioinformatics
#95
of 110 outputs
Altmetric has tracked 22,681,577 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 7,250 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 239,780 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 110 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.