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Mendeley readers
Attention Score in Context
| Title |
Semi-supervised protein subcellular localization
|
|---|---|
| Published in |
BMC Bioinformatics, January 2009
|
| DOI | 10.1186/1471-2105-10-s1-s47 |
| Pubmed ID | |
| Authors |
Qian Xu, Derek Hao Hu, Hong Xue, Weichuan Yu, Qiang Yang |
| Abstract |
Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data. |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Malaysia | 1 | 4% |
| United Kingdom | 1 | 4% |
| United States | 1 | 4% |
| Greece | 1 | 4% |
| Unknown | 19 | 83% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 35% |
| Professor > Associate Professor | 4 | 17% |
| Student > Bachelor | 4 | 17% |
| Other | 2 | 9% |
| Student > Doctoral Student | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 3 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 12 | 52% |
| Agricultural and Biological Sciences | 3 | 13% |
| Engineering | 2 | 9% |
| Biochemistry, Genetics and Molecular Biology | 1 | 4% |
| Medicine and Dentistry | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 3 | 13% |
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 03 May 2009.
All research outputs
#7,454,298
of 22,789,076 outputs
Outputs from BMC Bioinformatics
#3,023
of 7,279 outputs
Outputs of similar age
#49,625
of 170,523 outputs
Outputs of similar age from BMC Bioinformatics
#22
of 58 outputs
Altmetric has tracked 22,789,076 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 170,523 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.