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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks
|
|---|---|
| Published in |
BMC Systems Biology, October 2013
|
| DOI | 10.1186/1752-0509-7-s3-s9 |
| Pubmed ID | |
| Authors |
Zuoshuang Xiang, Tingting Qin, Zhaohui S Qin, Yongqun He |
| Abstract |
The large amount of literature in the post-genomics era enables the study of gene interactions and networks using all available articles published for a specific organism. MeSH is a controlled vocabulary of medical and scientific terms that is used by biomedical scientists to manually index articles in the PubMed literature database. We hypothesized that genome-wide gene-MeSH term associations from the PubMed literature database could be used to predict implicit gene-to-gene relationships and networks. While the gene-MeSH associations have been used to detect gene-gene interactions in some studies, different methods have not been well compared, and such a strategy has not been evaluated for a genome-wide literature analysis. Genome-wide literature mining of gene-to-gene interactions allows ranking of the best gene interactions and investigation of comprehensive biological networks at a genome level. |
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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 32 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 25% |
| Researcher | 7 | 22% |
| Student > Master | 3 | 9% |
| Student > Bachelor | 2 | 6% |
| Professor | 2 | 6% |
| Other | 4 | 13% |
| Unknown | 6 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 28% |
| Biochemistry, Genetics and Molecular Biology | 4 | 13% |
| Computer Science | 4 | 13% |
| Medicine and Dentistry | 4 | 13% |
| Nursing and Health Professions | 3 | 9% |
| Other | 3 | 9% |
| Unknown | 5 | 16% |
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 02 January 2015.
All research outputs
#15,314,171
of 22,776,824 outputs
Outputs from BMC Systems Biology
#644
of 1,142 outputs
Outputs of similar age
#129,729
of 210,819 outputs
Outputs of similar age from BMC Systems Biology
#15
of 26 outputs
Altmetric has tracked 22,776,824 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 32nd percentile – i.e., 32% 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 210,819 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.