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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective case–control cohort analysis
|
|---|---|
| Published in |
BMC Nephrology, July 2013
|
| DOI | 10.1186/1471-2369-14-162 |
| Pubmed ID | |
| Authors |
Ross KK Leung, Ying Wang, Ronald CW Ma, Andrea OY Luk, Vincent Lam, Maggie Ng, Wing Yee So, Stephen KW Tsui, Juliana CN Chan |
| Abstract |
Multi-causality and heterogeneity of phenotypes and genotypes characterize complex diseases. In a database with comprehensive collection of phenotypes and genotypes, we compared the performance of common machine learning methods to generate mathematical models to predict diabetic kidney disease (DKD). |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 123 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 123 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 17% |
| Student > Master | 20 | 16% |
| Researcher | 10 | 8% |
| Student > Bachelor | 9 | 7% |
| Professor | 8 | 7% |
| Other | 25 | 20% |
| Unknown | 30 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 32 | 26% |
| Medicine and Dentistry | 20 | 16% |
| Agricultural and Biological Sciences | 10 | 8% |
| Biochemistry, Genetics and Molecular Biology | 6 | 5% |
| Engineering | 6 | 5% |
| Other | 18 | 15% |
| Unknown | 31 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 23 July 2013.
All research outputs
#16,099,609
of 23,881,329 outputs
Outputs from BMC Nephrology
#1,509
of 2,550 outputs
Outputs of similar age
#124,956
of 200,760 outputs
Outputs of similar age from BMC Nephrology
#27
of 67 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,550 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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 200,760 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 67 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 55% of its contemporaries.