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Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer.
One of the greatest challenges facing human geneticists is the identification and characterization of susceptibility genes for common complex multifactorial human diseases. This challenge is partly…
Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions
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.
Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity
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.
Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases
- M. Ritchie, B. C. White, J. Parker, L. Hahn, J. Moore
- Computer Science, BiologyBMC Bioinformatics
- 7 July 2003
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.
Comparison of approaches for machine‐learning optimization of neural networks for detecting gene‐gene interactions in genetic epidemiology
- A. Motsinger-Reif, S. Dudek, L. Hahn, M. Ritchie
- Computer Science, BiologyGenetic epidemiology
- 1 May 2008
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.
Application Of Genetic Algorithms To The Discovery Of Complex Models For Simulation Studies In Human Genetics
This paper presents a strategy for identifying complex genetic models for simulation studies that utilizes genetic algorithms and demonstrates that the genetic algorithm approach routinely identifies interesting and useful penetrance functions in a human-competitve manner.
A novel method to identify gene–gene effects in nuclear families: the MDR‐PDT
A novel test, the multifactor dimensionality reduction‐PDT, is developed by merging the MDR method with the genotype‐Pedigree Disequilibrium Test (geno‐ PDT), which allows identification of single‐locus effects or joint effects of multiple loci in families of diverse structure.
Petri net modeling of high-order genetic systems using grammatical evolution.
Comparison of Neural Network Optimization Approaches for Studies of Human Genetics
This study demonstrates the utility of using GE to evolve NN in studies of complex human disease and compares the performance of GENN to GPNN, a traditional back-propagation neural network (BPNN) and a random search algorithm.
Measuring local context as context–word probabilities
- L. Hahn
- PsychologyBehavior research methods
- 31 May 2012
The present studies demonstrate that a composite context measure based on conditional probabilities for one- to four-word contexts and the presence of a final period represents all of the sentences and maintains significant correlations with cloze probabilities.