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Mendeley readers
Attention Score in Context
| Title |
PhenUMA: a tool for integrating the biomedical relationships among genes and diseases
|
|---|---|
| Published in |
BMC Bioinformatics, November 2014
|
| DOI | 10.1186/s12859-014-0375-1 |
| Pubmed ID | |
| Authors |
Rocío Rodríguez-López, Armando Reyes-Palomares, Francisca Sánchez-Jiménez, Miguel Ángel Medina |
| Abstract |
BackgroundSeveral types of genetic interactions in humans can be directly or indirectly associated with the causal effects of mutations. These interactions are usually based on their co-associations to biological processes, coexistence in cellular locations, coexpression in cell lines, physical interactions and so on. In addition, pathological processes can present similar phenotypes that have mutations either in the same genomic location or in different genomic regions. Therefore, integrative resources for all of these complex interactions can help us prioritize the relationships between genes and diseases that are most deserving to be studied by researchers and physicians.ResultsPhenUMA is a web application that displays biological networks using information from biomedical and biomolecular data repositories. One of its most innovative features is to combine the benefits of semantic similarity methods with the information taken from databases of genetic diseases and biological interactions. More specifically, this tool is useful in studying novel pathological relationships between functionally related genes, merging diseases into clusters that share specific phenotypes or finding diseases related to reported phenotypes.ConclusionsThis framework builds, analyzes and visualizes networks based on both functional and phenotypic relationships. The integration of this information helps in the discovery of alternative pathological roles of genes, biological functions and diseases. PhenUMA represents an advancement toward the use of new technologies for genomics and personalized medicine. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 50% |
| Norway | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 75% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 4% |
| Netherlands | 1 | 2% |
| Sweden | 1 | 2% |
| Unknown | 47 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 22% |
| Student > Ph. D. Student | 8 | 16% |
| Student > Bachelor | 5 | 10% |
| Student > Master | 5 | 10% |
| Student > Postgraduate | 4 | 8% |
| Other | 10 | 20% |
| Unknown | 8 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 13 | 25% |
| Agricultural and Biological Sciences | 12 | 24% |
| Computer Science | 8 | 16% |
| Medicine and Dentistry | 4 | 8% |
| Philosophy | 1 | 2% |
| Other | 4 | 8% |
| Unknown | 9 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 22 December 2014.
All research outputs
#13,416,718
of 22,771,140 outputs
Outputs from BMC Bioinformatics
#4,192
of 7,273 outputs
Outputs of similar age
#178,254
of 361,642 outputs
Outputs of similar age from BMC Bioinformatics
#64
of 136 outputs
Altmetric has tracked 22,771,140 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 38th percentile – i.e., 38% 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 361,642 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 136 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.