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Mendeley readers
Attention Score in Context
| Title |
Comparative genomics meets topology: a novel view on genome median and halving problems
|
|---|---|
| Published in |
BMC Bioinformatics, November 2016
|
| DOI | 10.1186/s12859-016-1263-7 |
| Pubmed ID | |
| Authors |
Nikita Alexeev, Pavel Avdeyev, Max A. Alekseyev |
| Abstract |
Genome median and genome halving are combinatorial optimization problems that aim at reconstruction of ancestral genomes by minimizing the number of evolutionary events between them and genomes of the extant species. While these problems have been widely studied in past decades, their solutions are often either not efficient or not biologically adequate. These shortcomings have been recently addressed by restricting the problems solution space. We show that the restricted variants of genome median and halving problems are, in fact, closely related. We demonstrate that these problems have a neat topological interpretation in terms of embedded graphs and polygon gluings. We illustrate how such interpretation can lead to solutions to these problems in particular cases. This study provides an unexpected link between comparative genomics and topology, and demonstrates advantages of solving genome median and halving problems within the topological framework. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 40% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 80% |
| Members of the public | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 4 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 2 | 50% |
| Professor > Associate Professor | 1 | 25% |
| Unknown | 1 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 2 | 50% |
| Unknown | 2 | 50% |
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 30 July 2017.
All research outputs
#12,678,879
of 22,901,818 outputs
Outputs from BMC Bioinformatics
#3,607
of 7,302 outputs
Outputs of similar age
#150,752
of 310,683 outputs
Outputs of similar age from BMC Bioinformatics
#41
of 125 outputs
Altmetric has tracked 22,901,818 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 7,302 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 310,683 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 51% of its contemporaries.
We're also able to compare this research output to 125 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.