title: A Mixture Dose-Response Model for Identifying High-Dimensional Drug Interaction Effects on Myopathy Using Electronic Medical Record Databases.
abstract: Interactions between multiple drugs may yield excessive risk of adverse effects. This increased risk is not uniform for all combinations, although some combinations may have constant adverse effect risks. We developed a statistical model using medical record data to identify drug combinations that induce myopathy risk. Such combinations are revealed using a novel mixture model, comprised of a constant risk model and a dose-response risk model. The dose represents the number of drug combinations. Using an empirical Bayes estimation method, we successfully identified high-dimensional (two to six) drug combinations that are associated with excessive myopathy risk at significantly low local false-discovery rates. From the curve of a dose-response model and high-dimensional drug interaction data, we observed that myopathy risk increases as the drug interaction dimension increases. This is the first time that such a dose-response relationship for high-dimensional drug interactions was observed and extracted from the medical record database.
authors.affiliation: Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Department of Biostatistics, Indiana University School of Medicine Indianapolis, Indiana, USA.
authors.forename: P
authors.initials: P
authors.lastname: Zhang
authors.affiliation: Departments of Radiology and Imaging Sciences, Indiana University School of Medicine Indianapolis, Indiana, USA.
authors.forename: L
authors.initials: L
authors.lastname: Du
authors.affiliation: Biomedical Engineering Institute, College of Automation, Harbin Engineering University Harbin, Heilongjiang, China ; Bioinformatics Research Center, College of Automation, Harbin Engineering University Harbin, Heilongjiang, China.
authors.forename: L
authors.initials: L
authors.lastname: Wang
authors.affiliation: Division Two, Department of Neurology, First Harbin Hospital Harbin, Heilongjiang, China.
authors.forename: M
authors.initials: M
authors.lastname: Liu
authors.affiliation: Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA.
authors.forename: L
authors.initials: L
authors.lastname: Cheng
authors.affiliation: Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA.
authors.forename: C-W
authors.initials: CW
authors.lastname: Chiang
authors.affiliation: Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA.
authors.forename: H-Y
authors.initials: HY
authors.lastname: Wu
authors.affiliation: Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Department of Obstetrics and Gynecology, Indiana University School of Medicine Indianapolis, Indiana, USA.
authors.forename: S K
authors.initials: SK
authors.lastname: Quinney
authors.affiliation: Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Departments of Radiology and Imaging Sciences, Indiana University School of Medicine Indianapolis, Indiana, USA.
authors.forename: L
authors.initials: L
authors.lastname: Shen
authors.affiliation: Center for Computational Biology and Bioinformatics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Department of Biostatistics, Indiana University School of Medicine Indianapolis, Indiana, USA ; Indiana Institute of Personalized Medicine, Indiana University School of Medicine Indianapolis, Indiana, USA.
authors.forename: L
authors.initials: L
authors.lastname: Li