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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A model-based information sharing protocol for profile Hidden Markov Models used for HIV-1 recombination detection
|
|---|---|
| Published in |
BMC Bioinformatics, June 2014
|
| DOI | 10.1186/1471-2105-15-205 |
| Pubmed ID | |
| Authors |
Ingo Bulla, Anne-Kathrin Schultz, Christophe Chesneau, Tanya Mark, Florin Serea |
| Abstract |
In many applications, a family of nucleotide or protein sequences classified into several subfamilies has to be modeled. Profile Hidden Markov Models (pHMMs) are widely used for this task, modeling each subfamily separately by one pHMM. However, a major drawback of this approach is the difficulty of dealing with subfamilies composed of very few sequences. One of the most crucial bioinformatical tasks affected by the problem of small-size subfamilies is the subtyping of human immunodeficiency virus type 1 (HIV-1) sequences, i.e., HIV-1 subtypes for which only a small number of sequences is known. |
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 % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 8% |
| Unknown | 12 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 38% |
| Student > Master | 2 | 15% |
| Student > Doctoral Student | 1 | 8% |
| Student > Ph. D. Student | 1 | 8% |
| Unknown | 4 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 3 | 23% |
| Computer Science | 3 | 23% |
| Nursing and Health Professions | 1 | 8% |
| Biochemistry, Genetics and Molecular Biology | 1 | 8% |
| Social Sciences | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 4 | 31% |
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 19 June 2014.
All research outputs
#20,231,820
of 22,757,541 outputs
Outputs from BMC Bioinformatics
#6,844
of 7,272 outputs
Outputs of similar age
#192,668
of 228,247 outputs
Outputs of similar age from BMC Bioinformatics
#144
of 155 outputs
Altmetric has tracked 22,757,541 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,272 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 1st percentile – i.e., 1% 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 228,247 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 155 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.