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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Gene-pseudogene evolution: a probabilistic approach
|
|---|---|
| Published in |
BMC Genomics, October 2015
|
| DOI | 10.1186/1471-2164-16-s10-s12 |
| Pubmed ID | |
| Authors |
Owais Mahmudi, Bengt Sennblad, Lars Arvestad, Katja Nowick, Jens Lagergren |
| Abstract |
Over the last decade, methods have been developed for the reconstruction of gene trees that take into account the species tree. Many of these methods have been based on the probabilistic duplication-loss model, which describes how a gene-tree evolves over a species-tree with respect to duplication and losses, as well as extension of this model, e.g., the DLRS (Duplication, Loss, Rate and Sequence evolution) model that also includes sequence evolution under relaxed molecular clock. A disjoint, almost as recent, and very important line of research has been focused on non protein-coding, but yet, functional DNA. For instance, DNA sequences being pseudogenes in the sense that they are not translated, may still be transcribed and the thereby produced RNA may be functional. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 1 | 50% |
| Switzerland | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Russia | 1 | 2% |
| Germany | 1 | 2% |
| Unknown | 41 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 19% |
| Student > Bachelor | 7 | 16% |
| Student > Ph. D. Student | 5 | 12% |
| Student > Master | 5 | 12% |
| Student > Doctoral Student | 4 | 9% |
| Other | 8 | 19% |
| Unknown | 6 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 15 | 35% |
| Agricultural and Biological Sciences | 13 | 30% |
| Computer Science | 3 | 7% |
| Medicine and Dentistry | 3 | 7% |
| Physics and Astronomy | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 7 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 07 October 2015.
All research outputs
#14,698,802
of 22,829,683 outputs
Outputs from BMC Genomics
#6,104
of 10,655 outputs
Outputs of similar age
#150,004
of 275,399 outputs
Outputs of similar age from BMC Genomics
#224
of 351 outputs
Altmetric has tracked 22,829,683 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,655 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 42nd percentile – i.e., 42% 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 275,399 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 351 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.