ArticleProceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology2006
Understanding the Evolutionary Process of Grammatical Evolution Neural Networks for Feature Selection in Genetic Epidemiology.
Article in Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
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- Novel EDGE encoding method enhances ability to identify genetic interactions.PLoS genetics · 2021Article
- Machine Learning Assisted Cervical Cancer Detection.Frontiers in public health · 2021Article
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- Genome-wide investigation of genetic changes during modern breeding of Brassica napus.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2014Article
- Statistical Optimization of Pharmacogenomics Association Studies: Key Considerations from Study Design to Analysis.Current pharmacogenomics and personalized medicine · 2011Article
- Rule based classifier for the analysis of gene-gene and gene-environment interactions in genetic association studies.BioData mining · 2011Article
- 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
- 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
- Exploring epistasis in candidate genes for rheumatoid arthritis.BMC proceedings · 2007Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The identification of genetic factors/features that predict complex diseases is an important goal of human genetics. The commonality of gene-gene interactions in the underlying genetic architecture of common diseases presents a daunting analytical challenge. Previously, we introduced a grammatical evolution neural network (GENN) approach that has high power to detect such interactions in the absence of any marginal main effects. While the success of this method is encouraging, it elicits questions regarding the evolutionary process of the algorithm itself and the feasibility of scaling the method to account for the immense dimensionality of datasets with enormous numbers of features. When the features of interest show no main effects, how is GENN able to build correct models? How and when should evolutionary parameters be adjusted according to the scale of a particular dataset? In the current study, we monitor the performance of GENN during its evolutionary process using different population sizes and numbers of generations. We also compare the evolutionary characteristics of GENN to that of a random search neural network strategy to better understand the benefits provided by the evolutionary learning process-including advantages with respect to chromosome size and the representation of functional versus non-functional features within the models generated by the two approaches. Finally, we apply lessons from the characterization of GENN to analyses of datasets containing increasing numbers of features to demonstrate the scalability of the method.
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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.