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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions
|
|---|---|
| Published in |
Biology Direct, April 2014
|
| DOI | 10.1186/1745-6150-9-5 |
| Pubmed ID | |
| Authors |
Hufeng Zhou, Shangzhi Gao, Nam Ninh Nguyen, Mengyuan Fan, Jingjing Jin, Bing Liu, Liang Zhao, Geng Xiong, Min Tan, Shijun Li, Limsoon Wong |
| Abstract |
H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are essential for understanding the infection mechanism of the formidable pathogen M. tuberculosis H37Rv. Computational prediction is an important strategy to fill the gap in experimental H. sapiens-M. tuberculosis H37Rv PPI data. Homology-based prediction is frequently used in predicting both intra-species and inter-species PPIs. However, some limitations are not properly resolved in several published works that predict eukaryote-prokaryote inter-species PPIs using intra-species template PPIs. |
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 % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 70 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 17% |
| Student > Ph. D. Student | 10 | 14% |
| Student > Bachelor | 6 | 9% |
| Student > Master | 5 | 7% |
| Student > Postgraduate | 3 | 4% |
| Other | 7 | 10% |
| Unknown | 27 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 17 | 24% |
| Agricultural and Biological Sciences | 9 | 13% |
| Computer Science | 7 | 10% |
| Chemistry | 2 | 3% |
| Environmental Science | 2 | 3% |
| Other | 2 | 3% |
| Unknown | 31 | 44% |
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 23 April 2014.
All research outputs
#13,407,734
of 22,753,345 outputs
Outputs from Biology Direct
#308
of 487 outputs
Outputs of similar age
#112,573
of 228,038 outputs
Outputs of similar age from Biology Direct
#1
of 2 outputs
Altmetric has tracked 22,753,345 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 487 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one is in the 33rd percentile – i.e., 33% 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 228,038 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them