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X Demographics
Mendeley readers
Attention Score in Context
| Title |
WISH-R– a fast and efficient tool for construction of epistatic networks for complex traits and diseases
|
|---|---|
| Published in |
BMC Bioinformatics, July 2018
|
| DOI | 10.1186/s12859-018-2291-2 |
| Pubmed ID | |
| Authors |
Victor A. O. Carmelo, Lisette J. A. Kogelman, Majbritt Busk Madsen, Haja N. Kadarmideen |
| Abstract |
Genetic epistasis is an often-overlooked area in the study of the genomics of complex traits. Genome-wide association studies are a useful tool for revealing potential causal genetic variants, but in this context, epistasis is generally ignored. Data complexity and interpretation issues make it difficult to process and interpret epistasis. As the number of interaction grows exponentially with the number of variants, computational limitation is a bottleneck. Gene Network based strategies have been successful in integrating biological data and identifying relevant hub genes and pathways related to complex traits. In this study, epistatic interactions and network-based analysis are combined in the Weighted Interaction SNP hub (WISH) method and implemented in an efficient and easy to use R package. The WISH R package (WISH-R) was developed to calculate epistatic interactions on a genome-wide level based on genomic data. It is easy to use and install, and works on regular genomic data. The package filters data based on linkage disequilibrium and calculates epistatic interaction coefficients between SNP pairs based on a parallelized efficient linear model and generalized linear model implementations. Normalized epistatic coefficients are analyzed in a network framework, alleviating multiple testing issues and integrating biological signal to identify modules and pathways related to complex traits. Functions for visualizing results and testing runtimes are also provided. The WISH-R package is an efficient implementation for analyzing genome-wide epistasis for complex diseases and traits. It includes methods and strategies for analyzing epistasis from initial data filtering until final data interpretation. WISH offers a new way to analyze genomic data by combining epistasis and network based analysis in one method and provides options for visualizations. This alleviates many of the existing hurdles in the analysis of genomic interactions. |
X Demographics
The data shown below were collected from the profiles of 8 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 | 2 | 25% |
| Denmark | 2 | 25% |
| Australia | 1 | 13% |
| India | 1 | 13% |
| Unknown | 2 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 5 | 63% |
| Members of the public | 3 | 38% |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 45 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 27% |
| Researcher | 8 | 18% |
| Student > Postgraduate | 4 | 9% |
| Student > Master | 4 | 9% |
| Student > Doctoral Student | 1 | 2% |
| Other | 4 | 9% |
| Unknown | 12 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 20% |
| Biochemistry, Genetics and Molecular Biology | 7 | 16% |
| Computer Science | 5 | 11% |
| Medicine and Dentistry | 3 | 7% |
| Neuroscience | 3 | 7% |
| Other | 5 | 11% |
| Unknown | 13 | 29% |
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 04 November 2020.
All research outputs
#6,135,244
of 23,098,660 outputs
Outputs from BMC Bioinformatics
#2,276
of 7,329 outputs
Outputs of similar age
#105,351
of 329,833 outputs
Outputs of similar age from BMC Bioinformatics
#32
of 100 outputs
Altmetric has tracked 23,098,660 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 7,329 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 68% 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 329,833 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 67% of its contemporaries.
We're also able to compare this research output to 100 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 68% of its contemporaries.