| Title |
A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification
|
|---|---|
| Published in |
Metabolomics, November 2019
|
| DOI | 10.1007/s11306-019-1612-4 |
| Pubmed ID | |
| Authors |
Kevin M. Mendez, Stacey N. Reinke, David I. Broadhurst |
X Demographics
The data shown below were collected from the profiles of 52 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 | 13 | 25% |
| United Kingdom | 5 | 10% |
| Spain | 3 | 6% |
| India | 2 | 4% |
| Australia | 2 | 4% |
| Canada | 1 | 2% |
| Germany | 1 | 2% |
| Korea, Republic of | 1 | 2% |
| Belgium | 1 | 2% |
| Other | 4 | 8% |
| Unknown | 19 | 37% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 24 | 46% |
| Members of the public | 22 | 42% |
| Practitioners (doctors, other healthcare professionals) | 3 | 6% |
| Science communicators (journalists, bloggers, editors) | 3 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 215 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 215 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 34 | 16% |
| Researcher | 30 | 14% |
| Student > Master | 23 | 11% |
| Student > Bachelor | 20 | 9% |
| Student > Doctoral Student | 12 | 6% |
| Other | 25 | 12% |
| Unknown | 71 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 26 | 12% |
| Agricultural and Biological Sciences | 23 | 11% |
| Computer Science | 17 | 8% |
| Chemistry | 17 | 8% |
| Engineering | 13 | 6% |
| Other | 40 | 19% |
| Unknown | 79 | 37% |
Attention Score in Context
This research output has an Altmetric Attention Score of 31. 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 06 May 2023.
All research outputs
#1,261,832
of 25,163,238 outputs
Outputs from Metabolomics
#39
of 1,373 outputs
Outputs of similar age
#26,793
of 366,221 outputs
Outputs of similar age from Metabolomics
#2
of 32 outputs
Altmetric has tracked 25,163,238 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,373 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 97% 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 366,221 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 32 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 96% of its contemporaries.