ArticleApplied soft computing2004
Routine Discovery of Complex Genetic Models using Genetic Algorithms.
Article in Applied soft computing, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed.
- A Novel Detection Method for High-Order SNP Epistatic Interactions Based on Explicit-Encoding-Based Multitasking Harmony Search.Interdisciplinary sciences, computational life sciences · 2024Article
- Heuristic identification of biological architectures for simulating complex hierarchical genetic interactions.Genetic epidemiology · 2015Article
- Cuckoo search epistasis: a new method for exploring significant genetic interactions.Heredity · 2014Article
- A classification and characterization of two-locus, pure, strict, epistatic models for simulation and detection.BioData mining · 2014Article
- GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures.BioData mining · 2012Article
- Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection.BioData mining · 2012Article
- Evolving hard problems: Generating human genetics datasets with a complex etiology.BioData mining · 2011Article
- Using biological knowledge to uncover the mystery in the search for epistasis in genome-wide association studies.Annals of human genetics · 2011Review
- Article
- A comparison of internal validation techniques for multifactor dimensionality reduction.BMC bioinformatics · 2010Article
- Simulating gene-environment interactions in complex human diseases.Genome medicine · 2010Article
- A general framework for formal tests of interaction after exhaustive search methods with applications to MDR and MDR-PDT.PloS one · 2010Article
- A cross-validation procedure for general pedigrees and matched odds ratio fitness metric implemented for the multifactor dimensionality reduction pedigree disequilibrium test.Genetic epidemiology · 2010Article
- Detecting purely epistatic multi-locus interactions by an omnibus permutation test on ensembles of two-locus analyses.BMC bioinformatics · 2009Article
- Exploring the performance of Multifactor Dimensionality Reduction in large scale SNP studies and in the presence of genetic heterogeneity among epistatic disease models.Human heredity · 2009Article
- Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2009Article
- Neural networks for genetic epidemiology: past, present, and future.BioData mining · 2008Article
- Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction.BMC bioinformatics · 2008Article
- Linkage Disequilibrium in Genetic Association Studies Improves the Performance of Grammatical Evolution Neural Networks.Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology · 2007Article
- Genetic Programming Neural Networks: A Powerful Bioinformatics Tool for Human Genetics.Applied soft computing · 2007Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes (i.e. epistasis or gene-gene interaction). Despite this need, the development of complex genetic models that can be used to simulate data is not always intuitive. In fact, only a few such models have been published. We have previously developed a genetic algorithm approach to discovering complex genetic models in which two single nucleotide polymorphisms (SNPs) influence disease risk solely through nonlinear interactions. In this paper, we extend this approach for the discovery of high-order epistasis models involving three to five SNPs. We demonstrate that the genetic algorithm is capable of routinely discovering interesting high-order epistasis models in which each SNP influences risk of disease only through interactions with the other SNPs in the model. This study opens the door for routine simulation of complex gene-gene interactions among SNPs for the development and evaluation of new statistical and computational approaches for identifying common, complex multifactorial disease susceptibility genes.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.