| Title |
The age-phenome database
|
|---|---|
| Published in |
SpringerPlus, April 2012
|
| DOI | 10.1186/2193-1801-1-4 |
| Pubmed ID | |
| Authors |
Nophar Geifman, Eitan Rubin |
| Abstract |
Data linking specific ages or age ranges with disease are abundant in biomedical literature. However, these data are organized such that searching for age-phenotype relationships is difficult. Recently, we described the Age-Phenome Knowledge-base (APK), a computational platform for storage and retrieval of information concerning age-related phenotypic patterns. Here, we report that data derived from over 1.5 million human-related PubMed abstracts have been added to APK. Using a text-mining pipeline, 35,683 entries which describe relationships between age and phenotype (such as disease) have been introduced into the database. Comparing the results to those obtained by a human reader reveals that the overall accuracy of these entries is estimated to exceed 80%. The usefulness of these data for obtaining new insight regarding age-disease relationships is demonstrated using clustering analysis, which is shown to capture obvious, as well as potentially interesting relationships between diseases. In addition, a new tool for browsing and searching the APK database is presented. We thus present a unique resource and a new framework for studying age-disease relationships and other phenotypic processes. |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 14% |
| Spain | 1 | 7% |
| Netherlands | 1 | 7% |
| Unknown | 10 | 71% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Doctoral Student | 2 | 14% |
| Student > Ph. D. Student | 2 | 14% |
| Other | 1 | 7% |
| Student > Bachelor | 1 | 7% |
| Professor | 1 | 7% |
| Other | 3 | 21% |
| Unknown | 4 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 4 | 29% |
| Agricultural and Biological Sciences | 3 | 21% |
| Computer Science | 1 | 7% |
| Immunology and Microbiology | 1 | 7% |
| Medicine and Dentistry | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 3 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 20 November 2012.
All research outputs
#5,873,643
of 22,761,738 outputs
Outputs from SpringerPlus
#343
of 1,852 outputs
Outputs of similar age
#40,071
of 163,106 outputs
Outputs of similar age from SpringerPlus
#2
of 2 outputs
Altmetric has tracked 22,761,738 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,852 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has done well, scoring higher than 81% 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 163,106 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.