ABSTRACT
With the rapid development of genome sequencing technology and bioinformatics in recent years,it has become possible to measure thousands of omics data which might be associated with the progress of diseases,i.e."high-dimensional data".This type of omics data have a common feature that the number of variable p is usually greater than the observation cases n,and often has high correlation between independent variables.Therefore,it is a great statistical challenge to identify really meaningful variables from omics data.This paper summarizes the methods of Bayesian variable selection in the analysis of high-dimensional data.
ABSTRACT
With the rapid development of genome sequencing technology and bioinformatics in recent years,it has become possible to measure thousands of omics data which might be associated with the progress of diseases,i.e."high-dimensional data".This type of omics data have a common feature that the number of variable p is usually greater than the observation cases n,and often has high correlation between independent variables.Therefore,it is a great statistical challenge to identify really meaningful variables from omics data.This paper summarizes the methods of Bayesian variable selection in the analysis of high-dimensional data.