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This becomes more challenging with a high-dimensional predictor set when there is the possibility of interaction.<\/jats:p><jats:p>Results: We demonstrate a novel non-parametric Bayes method based on a tensor factorization of predictor-dependent weights for Gaussian kernels. The method uses multistage predictor selection for dimension reduction, providing succinct models for the phenotype distribution. The resulting conditional density morphs flexibly with the selected predictors. In a simulation study and an application to molecular epidemiology data, we demonstrate advantages over commonly used methods.<\/jats:p><jats:p>Availability and implementation: MATLAB code available at https:\/\/googledrive.com\/host\/0Bw6KIFB-k4IOOWQ0dFJtSVZxNE0\/ktdctf.html<\/jats:p><jats:p>Contact: \u00a0dave.kessler@gmail.com<\/jats:p>","DOI":"10.1093\/bioinformatics\/btu040","type":"journal-article","created":{"date-parts":[[2014,2,6]],"date-time":"2014-02-06T01:45:54Z","timestamp":1391651154000},"page":"1562-1568","source":"Crossref","is-referenced-by-count":4,"title":["Learning phenotype densities conditional on many interacting predictors"],"prefix":"10.1093","volume":"30","author":[{"given":"David C.","family":"Kessler","sequence":"first","affiliation":[{"name":"1 Advanced Analytics Division, SAS Institute Inc., Cary, NC 27513, 2Molecular and Genetic Epidemiology Section, Epidemiology Branch and Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709 and 3Department of Statistical Science, Duke University, Durham, NC 27708"}]},{"given":"Jack A.","family":"Taylor","sequence":"additional","affiliation":[{"name":"1 Advanced Analytics Division, SAS Institute Inc., Cary, NC 27513, 2Molecular and Genetic Epidemiology Section, Epidemiology Branch and Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709 and 3Department of Statistical Science, Duke University, Durham, NC 27708"}]},{"given":"David B.","family":"Dunson","sequence":"additional","affiliation":[{"name":"1 Advanced Analytics Division, SAS Institute Inc., Cary, NC 27513, 2Molecular and Genetic Epidemiology Section, Epidemiology Branch and Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709 and 3Department of Statistical Science, Duke University, Durham, NC 27708"}]}],"member":"286","published-online":{"date-parts":[[2014,2,5]]},"reference":[{"key":"2023012710564736500_btu040-B1","first-page":"57","article-title":"Bayesian hierarchical mixtures of experts","volume-title":"Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence","author":"Bishop","year":"2003"},{"key":"2023012710564736500_btu040-B2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. 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