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Using Bayesian networks to discover relations between genes, environment, and disease.

, , , and . BioData Min., (2013)

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Estimation of environmental exposure: interpolation, kernel density estimation or snapshotting., , , , , , , and . Annals of GIS, 25 (1): 1-8 (2019)Functional dyadicity and heterophilicity of gene-gene interactions in statistical epistasis networks., , , and . BioData Min., (2015)Using Bayesian networks to discover relations between genes, environment, and disease., , , and . BioData Min., (2013)Research and applications: Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach., , , and . JAMIA, 20 (4): 603-612 (2013)Copy number variation has little impact on bead-array-based measures of DNA methylation., , , , , , , , , and . Bioinformatics, 25 (16): 1999-2005 (2009)Detecting gene-gene interactions using a permutation-based random forest method., , , , and . BioData Min., (2016)Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions., , , , , , , , , and 1 other author(s). BMC Bioinformatics, (2008)Supervising Random Forest Using Attribute Interaction Networks., , , , , and . EvoBIO, volume 7833 of Lecture Notes in Computer Science, page 104-116. Springer, (2013)Analysis of High-throughput DNA Methylation Bead Arrays Utilizing Bayesian Genotyping Algorithms., , , , , , , , , and 3 other author(s). BMEI (1), page 447-452. IEEE Computer Society, (2008)An Integrated Gaussian Graphical Model to evaluate the impact of exposures on metabolic networks., , , , , , , and . Comp. in Bio. and Med., (2019)