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X Demographics
Mendeley readers
Attention Score in Context
Title |
Cache-Oblivious parallel SIMD Viterbi decoding for sequence search in HMMER
|
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Published in |
BMC Bioinformatics, May 2014
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DOI | 10.1186/1471-2105-15-165 |
Pubmed ID | |
Authors |
Miguel Ferreira, Nuno Roma, Luis MS Russo |
Abstract |
HMMER is a commonly used bioinformatics tool based on Hidden Markov Models (HMMs) to analyze and process biological sequences. One of its main homology engines is based on the Viterbi decoding algorithm, which was already highly parallelized and optimized using Farrar's striped processing pattern with Intel SSE2 instruction set extension. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 1 | 33% |
Unknown | 2 | 67% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 15 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 6 | 40% |
Professor | 4 | 27% |
Researcher | 2 | 13% |
Student > Master | 2 | 13% |
Unknown | 1 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 6 | 40% |
Agricultural and Biological Sciences | 4 | 27% |
Biochemistry, Genetics and Molecular Biology | 1 | 7% |
Environmental Science | 1 | 7% |
Business, Management and Accounting | 1 | 7% |
Other | 0 | 0% |
Unknown | 2 | 13% |
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 25 June 2014.
All research outputs
#13,915,695
of 22,756,196 outputs
Outputs from BMC Bioinformatics
#4,470
of 7,272 outputs
Outputs of similar age
#116,184
of 226,629 outputs
Outputs of similar age from BMC Bioinformatics
#82
of 153 outputs
Altmetric has tracked 22,756,196 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% 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 35th percentile – i.e., 35% 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 226,629 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 153 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.