You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
A network embedding-based multiple information integration method for the MiRNA-disease association prediction
|
|---|---|
| Published in |
BMC Bioinformatics, September 2019
|
| DOI | 10.1186/s12859-019-3063-3 |
| Pubmed ID | |
| Authors |
Yuchong Gong, Yanqing Niu, Wen Zhang, Xiaohong Li |
X Demographics
The data shown below were collected from the profiles of 3 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 Kingdom | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 41 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 22% |
| Student > Master | 4 | 10% |
| Student > Postgraduate | 3 | 7% |
| Lecturer | 2 | 5% |
| Unspecified | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 17 | 41% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 11 | 27% |
| Biochemistry, Genetics and Molecular Biology | 5 | 12% |
| Agricultural and Biological Sciences | 3 | 7% |
| Unspecified | 2 | 5% |
| Social Sciences | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 18 | 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 01 October 2019.
All research outputs
#15,053,192
of 23,163,378 outputs
Outputs from BMC Bioinformatics
#5,098
of 7,342 outputs
Outputs of similar age
#199,752
of 340,828 outputs
Outputs of similar age from BMC Bioinformatics
#73
of 96 outputs
Altmetric has tracked 23,163,378 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,342 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 26th percentile – i.e., 26% 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 340,828 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 96 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.