| Title |
Analysis of the human diseasome using phenotype similarity between common, genetic and infectious diseases
|
|---|---|
| Published in |
Scientific Reports, June 2015
|
| DOI | 10.1038/srep10888 |
| Pubmed ID | |
| Authors |
Robert Hoehndorf, Paul N. Schofield, Georgios V. Gkoutos |
| Abstract |
Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are signs and symptoms of human Mendelian diseases in databases such as OMIM and Orphanet. Exploiting these resources, several computational methods have been developed for integration and analysis of phenotype data to identify the genetic etiology of diseases or suggest plausible interventions. A similar resource would be highly useful not only for rare and Mendelian diseases, but also for common, complex and infectious diseases. We apply a semantic text-mining approach to identify the phenotypes (signs and symptoms) associated with over 6,000 diseases. We evaluate our text-mined phenotypes by demonstrating that they can correctly identify known disease-associated genes in mice and humans with high accuracy. Using a phenotypic similarity measure, we generate a human disease network in which diseases that have similar signs and symptoms cluster together, and we use this network to identify closely related diseases based on common etiological, anatomical as well as physiological underpinnings. |
X Demographics
The data shown below were collected from the profiles of 23 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 3 | 13% |
| United Kingdom | 3 | 13% |
| United States | 3 | 13% |
| Korea, Republic of | 1 | 4% |
| South Africa | 1 | 4% |
| Comoros | 1 | 4% |
| Spain | 1 | 4% |
| Unknown | 10 | 43% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 13 | 57% |
| Scientists | 8 | 35% |
| Practitioners (doctors, other healthcare professionals) | 2 | 9% |
Mendeley readers
The data shown below were compiled from readership statistics for 146 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 3 | 2% |
| South Africa | 1 | <1% |
| Belgium | 1 | <1% |
| Denmark | 1 | <1% |
| Spain | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 138 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 37 | 25% |
| Researcher | 29 | 20% |
| Student > Master | 17 | 12% |
| Professor | 9 | 6% |
| Student > Doctoral Student | 7 | 5% |
| Other | 25 | 17% |
| Unknown | 22 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 38 | 26% |
| Biochemistry, Genetics and Molecular Biology | 25 | 17% |
| Computer Science | 22 | 15% |
| Medicine and Dentistry | 12 | 8% |
| Engineering | 5 | 3% |
| Other | 14 | 10% |
| Unknown | 30 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 24. 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 31 July 2020.
All research outputs
#1,605,793
of 25,728,855 outputs
Outputs from Scientific Reports
#15,221
of 142,667 outputs
Outputs of similar age
#19,568
of 280,727 outputs
Outputs of similar age from Scientific Reports
#164
of 1,857 outputs
Altmetric has tracked 25,728,855 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 142,667 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.8. This one has done well, scoring higher than 89% of its peers.
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