L
Lance W. Hahn
Researcher at Vanderbilt University
Publications - 34
Citations - 4412
Lance W. Hahn is an academic researcher from Vanderbilt University. The author has contributed to research in topics: Multifactor dimensionality reduction & Grammatical evolution. The author has an hindex of 14, co-authored 34 publications receiving 4233 citations. Previous affiliations of Lance W. Hahn include University of Texas at Austin & Western Kentucky University.
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Journal ArticleDOI
Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer
Marylyn D. Ritchie,Lance W. Hahn,Nady Roodi,L. Renee Bailey,William D. Dupont,Fritz F. Parl,Jason H. Moore +6 more
TL;DR: In this article, the authors introduced multifactor dimensionality reduction (MDR) as a method for reducing the dimensionality of multilocus information, to improve the identification of polymorphism combinations associated with disease risk.
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Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions.
TL;DR: A multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension thus permitting interactions to be detected in relatively small sample sizes is developed.
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Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity
TL;DR: Using simulated data, multifactor dimensionality reduction has high power to identify gene‐gene interactions in the presence of 5% genotyping error, 5% missing data, phenocopy, or a combination of both, and MDR has reduced power for some models in the Presence of 50% Phenocopy and very limited power in the absence of genetic heterogeneity.
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Optimizationof neural network architecture using genetic programming improvesdetection and modeling of gene-gene interactions in studies of humandiseases
TL;DR: This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases.
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Comparison of approaches for machine-learning optimization of neural networks for detecting gene-gene interactions in genetic epidemiology.
TL;DR: This study provides a detailed technical description of the use of grammatical evolution to optimize neural networks (GENN) for use in genetic association studies and shows that GENN greatly outperforms genetic programming neural networks in data sets with a large number of single nucleotide polymorphisms.