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Mendeley readers
Attention Score in Context
| Title |
Convergent algorithms for protein structural alignment
|
|---|---|
| Published in |
BMC Bioinformatics, August 2007
|
| DOI | 10.1186/1471-2105-8-306 |
| Pubmed ID | |
| Authors |
Leandro Martínez, Roberto Andreani, José Mario Martínez |
| Abstract |
Many algorithms exist for protein structural alignment, based on internal protein coordinates or on explicit superposition of the structures. These methods are usually successful for detecting structural similarities. However, current practical methods are seldom supported by convergence theories. In particular, although the goal of each algorithm is to maximize some scoring function, there is no practical method that theoretically guarantees score maximization. A practical algorithm with solid convergence properties would be useful for the refinement of protein folding maps, and for the development of new scores designed to be correlated with functional similarity. |
Mendeley readers
The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 4% |
| France | 2 | 3% |
| United States | 2 | 3% |
| Italy | 1 | 1% |
| Spain | 1 | 1% |
| Indonesia | 1 | 1% |
| Unknown | 65 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 28% |
| Researcher | 11 | 15% |
| Student > Bachelor | 7 | 9% |
| Professor | 7 | 9% |
| Professor > Associate Professor | 5 | 7% |
| Other | 13 | 17% |
| Unknown | 11 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 23 | 31% |
| Chemistry | 12 | 16% |
| Biochemistry, Genetics and Molecular Biology | 8 | 11% |
| Computer Science | 3 | 4% |
| Physics and Astronomy | 3 | 4% |
| Other | 13 | 17% |
| Unknown | 13 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 09 March 2023.
All research outputs
#7,729,343
of 23,505,669 outputs
Outputs from BMC Bioinformatics
#3,079
of 7,401 outputs
Outputs of similar age
#24,976
of 68,804 outputs
Outputs of similar age from BMC Bioinformatics
#15
of 43 outputs
Altmetric has tracked 23,505,669 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,401 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 68,804 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.