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Mendeley readers
Attention Score in Context
| Title |
Visualization of genetic disease-phenotype similarities by multiple maps t-SNE with Laplacian regularization
|
|---|---|
| Published in |
BMC Medical Genomics, October 2014
|
| DOI | 10.1186/1755-8794-7-s2-s1 |
| Pubmed ID | |
| Authors |
Weiwei Xu, Xingpeng Jiang, Xiaohua Hu, Guangrong Li |
| Abstract |
From a phenotypic standpoint, certain types of diseases may prove to be difficult to accurately diagnose, due to specific combinations of confounding symptoms. Referred to as phenotypic overlap, these sets of disease-related symptoms suggest shared pathophysiological mechanisms. Few attempts have been made to visualize the phenotypic relationships between different human diseases from a machine learning perspective. The proposed research, it is anticipated, will visually assist researchers in quickly disambiguating symptoms which can confound the timely and accurate diagnosis of a disease. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 37 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 27% |
| Researcher | 6 | 16% |
| Student > Bachelor | 3 | 8% |
| Other | 3 | 8% |
| Student > Master | 3 | 8% |
| Other | 8 | 22% |
| Unknown | 4 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 16% |
| Medicine and Dentistry | 5 | 14% |
| Computer Science | 4 | 11% |
| Agricultural and Biological Sciences | 4 | 11% |
| Mathematics | 2 | 5% |
| Other | 12 | 32% |
| Unknown | 4 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 17 August 2016.
All research outputs
#4,064,361
of 22,768,097 outputs
Outputs from BMC Medical Genomics
#190
of 1,222 outputs
Outputs of similar age
#47,108
of 260,342 outputs
Outputs of similar age from BMC Medical Genomics
#3
of 14 outputs
Altmetric has tracked 22,768,097 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,222 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 84% 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 260,342 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 81% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.