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Regularized instrumental variables estimators for disease classification.

Banff International Research Station for Mathematical Innovation and Discovery

Abstract: We have developed a novel approach under the framework of regularized instrumental variables estimators to build classifiers of a disease state. Instrumental variables estimators are analogous to classical regression estimators but they borrow strength from supplemental variables (the instruments) to address problems in the model, such as measurement errors and confounding factors, commonly encountered in genomics studies. Genetic data can be used as instruments in genomic biomarkers discovery studies to build tailored classifiers of a disease. Our approach can reduce the number of false positive discoveries and boost the identification of genomic biomarkers by exploiting and modeling the plausible biological mechanisms that relate genetics and gene expression information with a disease state. Authors: Joe Watson, David Kepplinger, and Gabriela Cohen Freue

Mathematics; Statistics; Computer science

video/mp4

Author affiliation: University of British Columbia

2019-05-07

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/70086

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/