Bayesian variable selection logistic regression with paired proteomic measurements.

ABSTRACT: We explore the problem of variable selection in a case-control setting with mass spectrometry proteomic data consisting of paired measurements. Each pair corresponds to a distinct isotope cluster and each component within pair represents a summary of isotopic expression based on either the intensity or the shape of the cluster. Our objective is to identify a collection of isotope clusters associated with the disease outcome and at the same time assess the predictive added-value of shape beyond intensity while maintaining predictive performance. We propose a Bayesian model that exploits the paired structure of our data and utilizes prior information on the relative predictive power of each source by introducing multiple layers of selection. This allows us to make simultaneous inference on w

SUBMITTER: Kakourou A 

PROVIDER: S-EPMC6175404 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature