Characterizing genetic interactions in human disease association studies using statistical epistasis networks
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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1471-2105
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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online resource.
- Bibliographic citation
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Characterizing genetic interactions in human disease association studies using statistical epistasis networks ; volume:12 ; number:1 ; day:12 ; month:9 ; year:2011 ; pages:1-13 ; date:12.2011
BMC bioinformatics ; 12, Heft 1 (12.9.2011), 1-13, 12.2011
- Creator
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Hu, Ting
- Contributor
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Sinnott-Armstrong, Nicholas A.
Kiralis, Jeff W.
Andrew, Angeline S.
Karagas, Margaret R.
Moore, Jason H.
SpringerLink (Online service)
- DOI
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10.1186/1471-2105-12-364
- URN
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urn:nbn:de:1111-2016021727816
- Rights
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Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:21 AM CEST
Data provider
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Associated
- Hu, Ting
- Sinnott-Armstrong, Nicholas A.
- Kiralis, Jeff W.
- Andrew, Angeline S.
- Karagas, Margaret R.
- Moore, Jason H.
- SpringerLink (Online service)
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Functional dyadicity and heterophilicity of gene-gene interactions in statistical epistasis networks
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Learning genetic epistasis using Bayesian network scoring criteria
Dominance vs epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles
Cancer type‐dependent genetic interactions between cancer driver alterations indicate plasticity of epistasis across cell types
wtest: an integrated R package for genetic epistasis testing
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Bayesian neural networks for detecting epistasis in genetic association studies
Contribution of genetic effects to genetic variance components with epistasis and linkage disequilibrium
Statistical power for detecting epistasis QTL effects under the F-2 design
Epistasis, Shapes and Evolution
Characterizing gene-gene interactions in a statistical epistasis network of twelve candidate genes for obesity
Functional dyadicity and heterophilicity of gene-gene interactions in statistical epistasis networks
Eigen-Epistasis for detecting gene-gene interactions
Learning genetic epistasis using Bayesian network scoring criteria
Dominance vs epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles
Cancer type‐dependent genetic interactions between cancer driver alterations indicate plasticity of epistasis across cell types
wtest: an integrated R package for genetic epistasis testing
Multifunctionality and epistasis in the evolution of gene regulatory interactions
Bayesian neural networks for detecting epistasis in genetic association studies
Contribution of genetic effects to genetic variance components with epistasis and linkage disequilibrium
Statistical power for detecting epistasis QTL effects under the F-2 design