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Mendeley readers
Attention Score in Context
| Title |
Weighted Interaction SNP Hub (WISH) network method for building genetic networks for complex diseases and traits using whole genome genotype data
|
|---|---|
| Published in |
BMC Systems Biology, March 2014
|
| DOI | 10.1186/1752-0509-8-s2-s5 |
| Pubmed ID | |
| Authors |
Lisette JA Kogelman, Haja N Kadarmideen |
| Abstract |
High-throughput genotype (HTG) data has been used primarily in genome-wide association (GWA) studies; however, GWA results explain only a limited part of the complete genetic variation of traits. In systems genetics, network approaches have been shown to be able to identify pathways and their underlying causal genes to unravel the biological and genetic background of complex diseases and traits, e.g., the Weighted Gene Co-expression Network Analysis (WGCNA) method based on microarray gene expression data. The main objective of this study was to develop a scale-free weighted genetic interaction network method using whole genome HTG data in order to detect biologically relevant pathways and potential genetic biomarkers for complex diseases and traits. |
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 % |
|---|---|---|
| United States | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| Norway | 1 | 1% |
| Switzerland | 1 | 1% |
| Unknown | 77 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 22% |
| Student > Ph. D. Student | 16 | 20% |
| Student > Master | 12 | 15% |
| Student > Postgraduate | 5 | 6% |
| Professor | 5 | 6% |
| Other | 11 | 14% |
| Unknown | 14 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 28 | 35% |
| Biochemistry, Genetics and Molecular Biology | 14 | 17% |
| Computer Science | 8 | 10% |
| Medicine and Dentistry | 4 | 5% |
| Mathematics | 2 | 2% |
| Other | 7 | 9% |
| Unknown | 18 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 19 January 2016.
All research outputs
#8,359,367
of 24,988,543 outputs
Outputs from BMC Systems Biology
#321
of 1,131 outputs
Outputs of similar age
#76,516
of 227,121 outputs
Outputs of similar age from BMC Systems Biology
#6
of 21 outputs
Altmetric has tracked 24,988,543 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,131 research outputs from this source. They receive a mean Attention Score of 3.7. This one has gotten more attention than average, scoring higher than 62% 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 227,121 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 52% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.