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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Deep GONet: self-explainable deep neural network based on Gene Ontology for phenotype prediction from gene expression data
|
|---|---|
| Published in |
BMC Bioinformatics, September 2021
|
| DOI | 10.1186/s12859-021-04370-7 |
| Pubmed ID | |
| Authors |
Victoria Bourgeais, Farida Zehraoui, Mohamed Ben Hamdoune, Blaise Hanczar |
X Demographics
The data shown below were collected from the profiles of 14 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 | 3 | 21% |
| India | 2 | 14% |
| Israel | 1 | 7% |
| Sweden | 1 | 7% |
| France | 1 | 7% |
| Unknown | 6 | 43% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 64% |
| Scientists | 5 | 36% |
Mendeley readers
The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 47 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 17% |
| Student > Master | 5 | 11% |
| Student > Bachelor | 3 | 6% |
| Student > Doctoral Student | 2 | 4% |
| Professor | 2 | 4% |
| Other | 8 | 17% |
| Unknown | 19 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 10 | 21% |
| Biochemistry, Genetics and Molecular Biology | 4 | 9% |
| Engineering | 3 | 6% |
| Agricultural and Biological Sciences | 2 | 4% |
| Unspecified | 1 | 2% |
| Other | 5 | 11% |
| Unknown | 22 | 47% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 13 October 2021.
All research outputs
#2,228,584
of 22,713,403 outputs
Outputs from BMC Bioinformatics
#641
of 7,259 outputs
Outputs of similar age
#52,928
of 429,623 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 149 outputs
Altmetric has tracked 22,713,403 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,259 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 91% 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 429,623 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 87% of its contemporaries.
We're also able to compare this research output to 149 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.