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Mendeley readers
Attention Score in Context
| Title |
Linking gene expression to phenotypes via pathway information
|
|---|---|
| Published in |
Journal of Biomedical Semantics, April 2015
|
| DOI | 10.1186/s13326-015-0013-5 |
| Pubmed ID | |
| Authors |
Irene Papatheodorou, Anika Oellrich, Damian Smedley |
| Abstract |
Establishing robust links among gene expression, pathways and phenotypes is critical for understanding diseases and developing treatments. In recent years there have been many efforts to develop the computational means to traverse from genes to gene expression, model pathways and classify phenotypes. Numerous ontologies and other controlled vocabularies have been developed, as well as computational methods to combine and mine these data sets and establish connections. Here we discuss these efforts and identify areas of future work that could lead to a better integration of genes, pathways and phenotypes to provide insights into the mechanisms under which gene mutations affect expression and pathways and how these effects are manifested onto the phenotype. |
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 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 1% |
| Korea, Republic of | 1 | 1% |
| Slovenia | 1 | 1% |
| Spain | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 93 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 20% |
| Researcher | 20 | 20% |
| Student > Master | 10 | 10% |
| Student > Bachelor | 9 | 9% |
| Professor > Associate Professor | 6 | 6% |
| Other | 15 | 15% |
| Unknown | 18 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 25 | 26% |
| Agricultural and Biological Sciences | 23 | 23% |
| Medicine and Dentistry | 7 | 7% |
| Computer Science | 6 | 6% |
| Engineering | 4 | 4% |
| Other | 10 | 10% |
| Unknown | 23 | 23% |
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 18 April 2019.
All research outputs
#18,407,102
of 22,800,560 outputs
Outputs from Journal of Biomedical Semantics
#299
of 364 outputs
Outputs of similar age
#193,303
of 264,712 outputs
Outputs of similar age from Journal of Biomedical Semantics
#13
of 14 outputs
Altmetric has tracked 22,800,560 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 364 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 8th percentile – i.e., 8% 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 264,712 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
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 is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.