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Mendeley readers
Attention Score in Context
| Title |
Tandem Repeats in Proteins: Prediction Algorithms and Biological Role
|
|---|---|
| Published in |
Frontiers in Bioengineering and Biotechnology, September 2015
|
| DOI | 10.3389/fbioe.2015.00143 |
| Pubmed ID | |
| Authors |
Marco Pellegrini |
| Abstract |
Tandem repetitions in protein sequence and structure is a fascinating subject of research which has been a focus of study since the late 1990s. In this survey, we give an overview on the multi-faceted aspects of research on protein tandem repeats (PTR for short), including prediction algorithms, databases, early classification efforts, mechanisms of PTR formation and evolution, and synthetic PTR design. We also touch on the rather open issue of the relationship between PTR and flexibility (or disorder) in proteins. Detection of PTR either from protein sequence or structure data is challenging due to inherent high (biological) signal-to-noise ratio that is a key feature of this problem. As early in silico analytic tools have been key enablers for starting this field of study, we expect that current and future algorithmic and statistical breakthroughs will have a high impact on the investigations of the biological role of PTR. |
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 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Korea, Republic of | 1 | 1% |
| Unknown | 70 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 28% |
| Researcher | 14 | 20% |
| Student > Master | 9 | 13% |
| Student > Bachelor | 7 | 10% |
| Professor | 4 | 6% |
| Other | 7 | 10% |
| Unknown | 10 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 28 | 39% |
| Agricultural and Biological Sciences | 16 | 23% |
| Computer Science | 6 | 8% |
| Engineering | 4 | 6% |
| Chemistry | 3 | 4% |
| Other | 5 | 7% |
| Unknown | 9 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 26 April 2020.
All research outputs
#7,223,059
of 22,829,083 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#1,169
of 6,561 outputs
Outputs of similar age
#88,770
of 274,665 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#11
of 63 outputs
Altmetric has tracked 22,829,083 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 6,561 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 81% of its peers.
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 274,665 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.