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Mendeley readers
Attention Score in Context
| Title |
Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment
|
|---|---|
| Published in |
BMC Systems Biology, January 2014
|
| DOI | 10.1186/1752-0509-8-5 |
| Pubmed ID | |
| Authors |
Wei-Po Lee, Yu-Ting Hsiao, Wei-Che Hwang |
| Abstract |
To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. |
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 % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| Brazil | 1 | 2% |
| Unknown | 49 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 22% |
| Student > Doctoral Student | 7 | 14% |
| Researcher | 7 | 14% |
| Student > Master | 7 | 14% |
| Student > Bachelor | 4 | 8% |
| Other | 7 | 14% |
| Unknown | 8 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 18 | 35% |
| Agricultural and Biological Sciences | 10 | 20% |
| Engineering | 6 | 12% |
| Medicine and Dentistry | 3 | 6% |
| Business, Management and Accounting | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 11 | 22% |
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 17 January 2014.
All research outputs
#20,674,485
of 25,394,764 outputs
Outputs from BMC Systems Biology
#827
of 1,132 outputs
Outputs of similar age
#242,891
of 320,066 outputs
Outputs of similar age from BMC Systems Biology
#37
of 50 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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We're also able to compare this research output to 50 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.