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Mendeley readers
Attention Score in Context
| Title |
Protein subcellular localization prediction of eukaryotes using a knowledge-based approach
|
|---|---|
| Published in |
BMC Bioinformatics, December 2009
|
| DOI | 10.1186/1471-2105-10-s15-s8 |
| Pubmed ID | |
| Authors |
Hsin-Nan Lin, Ching-Tai Chen, Ting-Yi Sung, Shinn-Ying Ho, Wen-Lian Hsu |
| Abstract |
The study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. However, determining the localization sites of a protein through wet-lab experiments can be time-consuming and labor-intensive. Thus, computational approaches become highly desirable. Most of the PSL prediction systems are established for single-localized proteins. However, a significant number of eukaryotic proteins are known to be localized into multiple subcellular organelles. Many studies have shown that proteins may simultaneously locate or move between different cellular compartments and be involved in different biological processes with different roles. |
Mendeley readers
The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 4% |
| India | 1 | 2% |
| United Kingdom | 1 | 2% |
| Italy | 1 | 2% |
| Iran, Islamic Republic of | 1 | 2% |
| New Zealand | 1 | 2% |
| Korea, Republic of | 1 | 2% |
| China | 1 | 2% |
| Unknown | 39 | 81% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 9 | 19% |
| Researcher | 8 | 17% |
| Student > Ph. D. Student | 8 | 17% |
| Student > Bachelor | 4 | 8% |
| Professor > Associate Professor | 3 | 6% |
| Other | 7 | 15% |
| Unknown | 9 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 29% |
| Computer Science | 12 | 25% |
| Biochemistry, Genetics and Molecular Biology | 9 | 19% |
| Linguistics | 1 | 2% |
| Mathematics | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 10 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 31 December 2016.
All research outputs
#7,453,350
of 22,786,087 outputs
Outputs from BMC Bioinformatics
#3,023
of 7,279 outputs
Outputs of similar age
#48,390
of 165,484 outputs
Outputs of similar age from BMC Bioinformatics
#23
of 47 outputs
Altmetric has tracked 22,786,087 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,279 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% of its peers.
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 165,484 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.