ArticleBioinformatics (Oxford, England)2005
Mining genetic epidemiology data with Bayesian networks I: Bayesian networks and example application (plasma apoE levels).
Article in Bioinformatics (Oxford, England), 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
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- An Investigation into the Temporal Reproducibility of Tryptophan Metabolite Networks Among Healthy Adolescents.International journal of tryptophan research : IJTR · 2021Article
- Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective.Metabolites · 2019Review
- Generating a robust statistical causal structure over 13 cardiovascular disease risk factors using genomics data.Journal of biomedical informatics · 2016Article
- Prediction of fetal hemoglobin in sickle cell anemia using an ensemble of genetic risk prediction models.Circulation. Cardiovascular genetics · 2014Article
- Metabolomic derangements are associated with mortality in critically ill adult patients.PloS one · 2014Article
- Evaluation of an ensemble of genetic models for prediction of a quantitative trait.Frontiers in genetics · 2014Article
- Network-based identification of biomarkers coexpressed with multiple pathways.Cancer informatics · 2014Review
- The St. Louis African American health-heart study: methodology for the study of cardiovascular disease and depression in young-old African Americans.BMC cardiovascular disorders · 2013Article
- A sparse structure learning algorithm for Gaussian Bayesian Network identification from high-dimensional data.IEEE transactions on pattern analysis and machine intelligence · 2013Article
- Using Bayesian networks to discover relations between genes, environment, and disease.BioData mining · 2013Article
- Integrative systems biology approaches in asthma pharmacogenomics.Pharmacogenomics · 2012Review
- Naïve Bayesian Classifier and Genetic Risk Score for Genetic Risk Prediction of a Categorical Trait: Not so Different after all!Frontiers in genetics · 2012Article
- Model averaging strategies for structure learning in Bayesian networks with limited data.BMC bioinformatics · 2012Article
- Network Medicine: New Paradigm in the -Omics Era.Anatomy & physiology : current research · 2011Article
- Application of Bayesian network structure learning to identify causal variant SNPs from resequencing data.BMC proceedings · 2011Article
- Latent variable modeling paradigms for genotype-trait association studies.Biometrical journal. Biometrische Zeitschrift · 2011Article
- Association analyses of the MAS-QTL data set using grammar, principal components and Bayesian network methodologies.BMC proceedings · 2011Article
- Causal graph-based analysis of genome-wide association data in rheumatoid arthritis.Biology direct · 2011Article
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2 authors.
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Abstract
motivationThe wealth of single nucleotide polymorphism (SNP) data within candidate genes and anticipated across the genome poses enormous analytical problems for studies of genotype-to-phenotype relationships, and modern data mining methods may be particularly well suited to meet the swelling challenges. In this paper, we introduce the method of Belief (Bayesian) networks to the domain of genotype-to-phenotype analyses and provide an example application.
resultsA Belief network is a graphical model of a probabilistic nature that represents a joint multivariate probability distribution and reflects conditional independences between variables. Given the data, optimal network topology can be estimated with the assistance of heuristic search algorithms and scoring criteria. Statistical significance of edge strengths can be evaluated using Bayesian methods and bootstrapping. As an example application, the method of Belief networks was applied to 20 SNPs in the apolipoprotein (apo) E gene and plasma apoE levels in a sample of 702 individuals from Jackson, MS. Plasma apoE level was the primary target variable. These analyses indicate that the edge between SNP 4075, coding for the well-known epsilon2 allele, and plasma apoE level was strong. Belief networks can effectively describe complex uncertain processes and can both learn from data and incorporate prior knowledge. AVAILABILITY: Various alternative and supplemental networks (not given in the text) as well as source code extensions, are available from the authors. SUPPLEMENTARY INFORMATION: http://bioinformatics.oxfordjournals.org.
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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.