Bayesian modelling and Implementation of longitudinal gene expression data
Category: Young Statisticians
Abstract
Time-varying biomarkers have become an effective indicator for identifying the disease progression in Oncology. This tool has been proven advantageous to detect tumor development at early stages and also benefits in treatment decision making. In practice, the information of expression values is collected for a large number of biomarkers, producing a high-dimensional structure of the dataset. However, these datasets often contain missing observations due to patient drop-outs which undermines the validity of the research results. Our research aims towards several challenges that appeared in the biomarker studies and develops an effective statistical procedure for classification and modeling of gene expression data.