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X Demographics
Mendeley readers
Attention Score in Context
| Title |
SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec
|
|---|---|
| Published in |
BMC Bioinformatics, November 2021
|
| DOI | 10.1186/s12859-021-04457-1 |
| Pubmed ID | |
| Authors |
Jianwei Li, Jianing Li, Mengfan Kong, Duanyang Wang, Kun Fu, Jiangcheng Shi |
X Demographics
The data shown below were collected from the profiles of 5 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 States | 2 | 40% |
| India | 1 | 20% |
| United Kingdom | 1 | 20% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 2 | 17% |
| Student > Ph. D. Student | 1 | 8% |
| Other | 1 | 8% |
| Student > Doctoral Student | 1 | 8% |
| Student > Master | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 6 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 25% |
| Biochemistry, Genetics and Molecular Biology | 2 | 17% |
| Unspecified | 2 | 17% |
| Unknown | 5 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 30 November 2021.
All research outputs
#4,147,658
of 24,998,746 outputs
Outputs from BMC Bioinformatics
#1,427
of 7,630 outputs
Outputs of similar age
#85,887
of 435,469 outputs
Outputs of similar age from BMC Bioinformatics
#46
of 173 outputs
Altmetric has tracked 24,998,746 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,630 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 80% 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 435,469 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 173 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.