ArticleProceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference2002
Application of Genetic Algorithms to the Discovery of Complex Models for Simulation Studies in Human Genetics.
Article in Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference, 2002. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
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Who cites it
23 citing papers in PubMed.
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- Filter-free exhaustive odds ratio-based genome-wide interaction approach pinpoints evidence for interaction in the HLA region in psoriasis.BMC genetics · 2015Article
- 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
- iLOCi: a SNP interaction prioritization technique for detecting epistasis in genome-wide association studies.BMC genomics · 2012Article
- Entropy-based information gain approaches to detect and to characterize gene-gene and gene-environment interactions/correlations of complex diseases.Genetic epidemiology · 2011Article
- Evolving hard problems: Generating human genetics datasets with a complex etiology.BioData mining · 2011Article
- Gene-gene interaction filtering with ensemble of filters.BMC bioinformatics · 2011Article
- Multifactor dimensionality reduction as a filter-based approach for genome wide association studies.Frontiers in genetics · 2011Article
- A genetic ensemble approach for gene-gene interaction identification.BMC bioinformatics · 2010Article
- ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci.BioData mining · 2010Article
- Reconstructability analysis as a tool for identifying gene-gene interactions in studies of human diseases.Statistical applications in genetics and molecular biology · 2010Article
- Methods for optimizing statistical analyses in pharmacogenomics research.Expert review of clinical pharmacology · 2009Article
- Power of grammatical evolution neural networks to detect gene-gene interactions in the presence of error.BMC research notes · 2008Article
- Neural networks for genetic epidemiology: past, present, and future.BioData mining · 2008Article
- A Balanced Accuracy Fitness Function Leads to Robust Analysis using Grammatical Evolution Neural Networks in the Case of Class Imbalance.Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference · 2008Article
- Understanding the Evolutionary Process of Grammatical Evolution Neural Networks for Feature Selection in Genetic Epidemiology.Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology · 2006Article
- Multifactor dimensionality reduction: an analysis strategy for modelling and detecting gene-gene interactions in human genetics and pharmacogenomics studies.Human genomics · 2006Review
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. 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. In this paper, we present a strategy for identifying complex genetic models for simulation studies that utilizes genetic algorithms. The genetic models used in this study are penetrance functions that define the probability of disease given a specific DNA sequence variation has been inherited. We demonstrate that the genetic algorithm approach routinely identifies interesting and useful penetrance functions in a human-competitve manner.
Identifiers
23413413PMC3569849What OpenQuestion holds
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.