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X Demographics
Mendeley readers
Attention Score in Context
| Title |
CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications
|
|---|---|
| Published in |
BMC Genomics, January 2012
|
| DOI | 10.1186/1471-2164-13-s1-s14 |
| Pubmed ID | |
| Authors |
Guoqing Lei, Yong Dou, Wen Wan, Fei Xia, Rongchun Li, Meng Ma, Dan Zou |
| Abstract |
Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. |
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 % |
|---|---|---|
| France | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 3% |
| South Africa | 1 | 3% |
| United Kingdom | 1 | 3% |
| Canada | 1 | 3% |
| Sri Lanka | 1 | 3% |
| Unknown | 29 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 24% |
| Student > Master | 8 | 24% |
| Student > Ph. D. Student | 6 | 18% |
| Student > Bachelor | 2 | 6% |
| Professor | 2 | 6% |
| Other | 6 | 18% |
| Unknown | 2 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 13 | 38% |
| Agricultural and Biological Sciences | 9 | 26% |
| Biochemistry, Genetics and Molecular Biology | 7 | 21% |
| Engineering | 3 | 9% |
| Unknown | 2 | 6% |
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 22 August 2012.
All research outputs
#22,758,309
of 25,373,627 outputs
Outputs from BMC Genomics
#9,840
of 11,244 outputs
Outputs of similar age
#229,208
of 251,197 outputs
Outputs of similar age from BMC Genomics
#113
of 118 outputs
Altmetric has tracked 25,373,627 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 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. 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 251,197 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 118 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.