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Mendeley readers
Attention Score in Context
| Title |
Efficacy of different protein descriptors in predicting protein functional families
|
|---|---|
| Published in |
BMC Bioinformatics, August 2007
|
| DOI | 10.1186/1471-2105-8-300 |
| Pubmed ID | |
| Authors |
Serene AK Ong, Hong Huang Lin, Yu Zong Chen, Ze Rong Li, Zhiwei Cao |
| Abstract |
Sequence-derived structural and physicochemical descriptors have frequently been used in machine learning prediction of protein functional families, thus there is a need to comparatively evaluate the effectiveness of these descriptor-sets by using the same method and parameter optimization algorithm, and to examine whether the combined use of these descriptor-sets help to improve predictive performance. Six individual descriptor-sets and four combination-sets were evaluated in support vector machines (SVM) prediction of six protein functional families. |
Mendeley readers
The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 1 | <1% |
| Italy | 1 | <1% |
| Israel | 1 | <1% |
| United Kingdom | 1 | <1% |
| Iran, Islamic Republic of | 1 | <1% |
| Romania | 1 | <1% |
| Unknown | 98 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 20% |
| Researcher | 15 | 14% |
| Student > Bachelor | 14 | 13% |
| Student > Master | 12 | 12% |
| Student > Doctoral Student | 5 | 5% |
| Other | 14 | 13% |
| Unknown | 23 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 21 | 20% |
| Agricultural and Biological Sciences | 20 | 19% |
| Biochemistry, Genetics and Molecular Biology | 13 | 13% |
| Chemistry | 8 | 8% |
| Engineering | 5 | 5% |
| Other | 11 | 11% |
| Unknown | 26 | 25% |
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 22 July 2010.
All research outputs
#12,846,160
of 22,649,029 outputs
Outputs from BMC Bioinformatics
#3,776
of 7,234 outputs
Outputs of similar age
#55,992
of 67,598 outputs
Outputs of similar age from BMC Bioinformatics
#29
of 44 outputs
Altmetric has tracked 22,649,029 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,234 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 45th percentile – i.e., 45% 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 67,598 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 44 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.