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Epistasis

Overview of attention for book
Cover of 'Epistasis'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Long-Term Selection Experiments: Epistasis and the Response to Selection
  3. Altmetric Badge
    Chapter 2 Finding the Epistasis Needles in the Genome-Wide Haystack
  4. Altmetric Badge
    Chapter 3 Biological Knowledge-Driven Analysis of Epistasis in Human GWAS with Application to Lipid Traits.
  5. Altmetric Badge
    Chapter 4 Epistasis for quantitative traits in Drosophila.
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    Chapter 5 Epistasis in the risk of human neuropsychiatric disease.
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    Chapter 6 On the Partitioning of Genetic Variance with Epistasis
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    Chapter 7 Measuring gene interactions.
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    Chapter 8 Two Rules for the Detection and Quantification of Epistasis and Other Interaction Effects
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    Chapter 9 Direct Approach to Modeling Epistasis
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    Chapter 10 Capacitating Epistasis—Detection and Role in the Genetic Architecture of Complex Traits
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    Chapter 11 Compositional Epistasis: An Epidemiologic Perspective
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    Chapter 12 Identification of Genome-Wide SNP-SNP and SNP-Clinical Boolean Interactions in Age-Related Macular Degeneration.
  14. Altmetric Badge
    Chapter 13 Epistasis Analysis Using Information Theory
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    Chapter 14 Genome-wide epistasis and pleiotropy characterized by the bipartite human phenotype network.
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    Chapter 15 Network theory for data-driven epistasis networks.
  17. Altmetric Badge
    Chapter 16 Epistasis analysis using multifactor dimensionality reduction.
  18. Altmetric Badge
    Chapter 17 Epistasis Analysis Using ReliefF
  19. Altmetric Badge
    Chapter 18 Epistasis analysis using artificial intelligence.
Attention for Chapter 15: Network theory for data-driven epistasis networks.
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

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Chapter title
Network theory for data-driven epistasis networks.
Chapter number 15
Book title
Epistasis
Published in
Methods in molecular biology, January 2015
DOI 10.1007/978-1-4939-2155-3_15
Pubmed ID
Book ISBNs
978-1-4939-2154-6, 978-1-4939-2155-3
Authors

Caleb A Lareau, Brett A McKinney, Caleb A. Lareau, Brett A. McKinney, Lareau, Caleb A., McKinney, Brett A.

Abstract

One of the challenges of understanding the genetic basis of complex phenotypes is explaining variability not attributable to individual genes. While most existing methods that investigate variant mutations or differential gene expression focus on individual effects, a complex system of gene interactions (epistasis) and pathways is likely needed to explain phenotypic variation. Herein, we examine methods for treating the interactions in these biological data sets as edges in a network model of the phenotype and review relevant network theory methods for analyzing network structure and identifying important genes. In particular, we review methods for detecting community structure, describing the statistical properties of networks, and computing network centrality of genes that may reveal insights missed by individual genetic effects. We also discuss available tools to facilitate the construction and visualization of epistasis networks of GWAS data.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 9%
Denmark 1 9%
Unknown 9 82%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 36%
Researcher 4 36%
Professor 1 9%
Unspecified 1 9%
Student > Master 1 9%
Other 0 0%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 55%
Biochemistry, Genetics and Molecular Biology 2 18%
Mathematics 1 9%
Computer Science 1 9%
Unspecified 1 9%
Other 0 0%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 06 February 2015.
All research outputs
#6,699,959
of 22,771,140 outputs
Outputs from Methods in molecular biology
#1,994
of 13,090 outputs
Outputs of similar age
#91,767
of 352,903 outputs
Outputs of similar age from Methods in molecular biology
#153
of 996 outputs
Altmetric has tracked 22,771,140 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 13,090 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done well, scoring higher than 84% 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 352,903 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 73% of its contemporaries.
We're also able to compare this research output to 996 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.