Integrating multi-platform genomic data using hierarchical Bayesian relevance vector machines.

ABSTRACT:

Background

Recent advances in genome technologies and the subsequent collection of genomic information at various molecular resolutions hold promise to accelerate the discovery of new therapeutic targets. A critical step in achieving these goals is to develop efficient clinical prediction models that integrate these diverse sources of high-throughput data. This step is challenging due to the presence of high-dimensionality and complex interactions in the data. For predicting relevant clinical outcomes, we propose a flexible statistical machine learning approach that acknowledges and models the interaction between platform-specific measurements through nonlinear kernel machines and borrows information within and between platforms through a hierarchical Bayesian framework. Our model

SUBMITTER: Srivastava S 

PROVIDER: S-EPMC3726335 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature