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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Predicting clinically promising therapeutic hypotheses using tensor factorization
|
|---|---|
| Published in |
BMC Bioinformatics, February 2019
|
| DOI | 10.1186/s12859-019-2664-1 |
| Pubmed ID | |
| Authors |
Jin Yao, Mark R. Hurle, Matthew R. Nelson, Pankaj Agarwal |
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 Kingdom | 2 | 40% |
| Russia | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 60% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
| Members of the public | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 55 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 24% |
| Student > Ph. D. Student | 7 | 13% |
| Student > Bachelor | 4 | 7% |
| Student > Master | 4 | 7% |
| Professor | 3 | 5% |
| Other | 8 | 15% |
| Unknown | 16 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 11 | 20% |
| Computer Science | 9 | 16% |
| Medicine and Dentistry | 3 | 5% |
| Agricultural and Biological Sciences | 3 | 5% |
| Engineering | 3 | 5% |
| Other | 8 | 15% |
| Unknown | 18 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 January 2024.
All research outputs
#3,083,684
of 25,107,281 outputs
Outputs from BMC Bioinformatics
#962
of 7,652 outputs
Outputs of similar age
#69,982
of 451,901 outputs
Outputs of similar age from BMC Bioinformatics
#29
of 178 outputs
Altmetric has tracked 25,107,281 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,652 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 87% 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 451,901 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 84% of its contemporaries.
We're also able to compare this research output to 178 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.