A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility

DOI10.1016/j.jtbi.2005.11.036zbMath1447.92258OpenAlexW2076220518WikidataQ28295511 ScholiaQ28295511MaRDI QIDQ2199193

Fu-Tien Chiang, Joshua C. Gilbert, Jason H. Moore, Todd Holden, Bill C. White, Chia-Ti Tsai, Nate Barney

Publication date: 16 September 2020

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jtbi.2005.11.036

zbMATH Keywords

entropydata miningmachine learningconstructive inductiongene-gene interactionsmultifactor dimensionality reduction

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Genetics and epigenetics (92D10) Statistical aspects of big data and data science (62R07) Pathology, pathophysiology (92C32)

Related Items (4)

Building up a robust risk mathematical platform to predict colorectal cancer ⋮ Asymptotic distributions of empirical interaction information ⋮ A general framework for and new normalization of attributable proportion ⋮ An epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria

Uses Software

• ElemStatLearn
• MDR
• ReliefF

Cites Work

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• Optimization by Simulated Annealing
• Hypothesis-driven constructive induction in AQ17-HCI: A method and experiments
• Inference for the generalization error
• Theoretical and empirical analysis of ReliefF and RReliefF
• Permutation tests. A practical guide to resampling methods for testing hypotheses.
• Multivariante information transmission
• The elements of statistical learning. Data mining, inference, and prediction

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