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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Minimalist ensemble algorithms for genome-wide protein localization prediction
|
|---|---|
| Published in |
BMC Bioinformatics, July 2012
|
| DOI | 10.1186/1471-2105-13-157 |
| Pubmed ID | |
| Authors |
Jhih-Rong Lin, Ananda Mohan Mondal, Rong Liu, Jianjun Hu |
| Abstract |
Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. |
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 % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| India | 1 | 2% |
| Czechia | 1 | 2% |
| Unknown | 46 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 27% |
| Researcher | 10 | 20% |
| Student > Master | 7 | 14% |
| Professor > Associate Professor | 3 | 6% |
| Student > Bachelor | 2 | 4% |
| Other | 7 | 14% |
| Unknown | 7 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 24 | 49% |
| Biochemistry, Genetics and Molecular Biology | 9 | 18% |
| Computer Science | 4 | 8% |
| Business, Management and Accounting | 1 | 2% |
| Immunology and Microbiology | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 8 | 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 06 July 2012.
All research outputs
#14,147,011
of 22,669,724 outputs
Outputs from BMC Bioinformatics
#4,712
of 7,247 outputs
Outputs of similar age
#96,620
of 164,352 outputs
Outputs of similar age from BMC Bioinformatics
#55
of 92 outputs
Altmetric has tracked 22,669,724 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 7,247 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 30th percentile – i.e., 30% 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 164,352 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 92 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.