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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data
|
|---|---|
| Published in |
BMC Bioinformatics, March 2020
|
| DOI | 10.1186/s12859-020-3427-8 |
| Pubmed ID | |
| Authors |
Aaron M. Smith, Jonathan R. Walsh, John Long, Craig B. Davis, Peter Henstock, Martin R. Hodge, Mateusz Maciejewski, Xinmeng Jasmine Mu, Stephen Ra, Shanrong Zhao, Daniel Ziemek, Charles K. Fisher |
X Demographics
The data shown below were collected from the profiles of 49 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 | 18 | 37% |
| Germany | 3 | 6% |
| Switzerland | 2 | 4% |
| India | 2 | 4% |
| Benin | 1 | 2% |
| Sweden | 1 | 2% |
| Israel | 1 | 2% |
| Australia | 1 | 2% |
| United Kingdom | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 18 | 37% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 30 | 61% |
| Scientists | 18 | 37% |
| Practitioners (doctors, other healthcare professionals) | 1 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 177 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 177 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 34 | 19% |
| Researcher | 21 | 12% |
| Student > Master | 20 | 11% |
| Student > Bachelor | 11 | 6% |
| Student > Postgraduate | 6 | 3% |
| Other | 21 | 12% |
| Unknown | 64 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 38 | 21% |
| Agricultural and Biological Sciences | 18 | 10% |
| Computer Science | 18 | 10% |
| Medicine and Dentistry | 11 | 6% |
| Engineering | 8 | 5% |
| Other | 17 | 10% |
| Unknown | 67 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 34. 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 16 August 2023.
All research outputs
#1,216,964
of 25,845,895 outputs
Outputs from BMC Bioinformatics
#117
of 7,756 outputs
Outputs of similar age
#31,109
of 392,828 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 119 outputs
Altmetric has tracked 25,845,895 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,756 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done particularly well, scoring higher than 98% 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 392,828 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 119 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 99% of its contemporaries.