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Chapter title |
Integrating Multidimensional Data Sources to Identify Genes Regulating Complex Phenotypes
|
---|---|
Chapter number | 10 |
Book title |
Systems Genetics
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6427-7_10 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6425-3, 978-1-4939-6427-7
|
Authors |
Rupert W. Overall, Overall, Rupert W |
Editors |
Klaus Schughart, Robert W. Williams |
Abstract |
Phenotypes collected with a view to quantitative trait locus mapping can be augmented with compatible whole-transcriptome expression data and information from several other sources. These different data sources can be assembled into multidimensional network models which allow the identification of key genes potentially driving the phenotype of interest. The following chapter describes this approach using an example workflow. Several alternatives and potential limitations are discussed to aid the researcher when applying these techniques to their own work. |
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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 29 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Professor > Associate Professor | 1 | 3% |
Other | 1 | 3% |
Unknown | 27 | 93% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 1 | 3% |
Agricultural and Biological Sciences | 1 | 3% |
Unknown | 27 | 93% |
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 11 December 2016.
All research outputs
#15,402,296
of 22,912,409 outputs
Outputs from Methods in molecular biology
#5,359
of 13,131 outputs
Outputs of similar age
#256,356
of 420,471 outputs
Outputs of similar age from Methods in molecular biology
#466
of 1,074 outputs
Altmetric has tracked 22,912,409 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 13,131 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% 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 420,471 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,074 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.