Supervised Learning of Bayesian Network Parameters Made Easy (original) (raw)

Bayesian Models for Variable Selection that Incorporate Biological Information (with discussion)

Variable selection has been the focus of much research in recent years. Bayesian methods have found many successful applications, particularly in situations where the amount of measured variables can be much greater than the number of observations. One such example is the analysis of genomics data. In this paper we first review Bayesian variable selection methods for linear settings, including regression and classi fication models. We focus in particular on recent prior constructions that have been used for the analysis of genomic data and briefly describe two novel applications that integrate di fferent sources of biological information into the analysis of experimental data. Next, we address variable selection for a di fferent modeling context, i.e. mixture models. We address both clustering and discriminant analysis settings and conclude with an application to gene expression data for patients a ffected by leukemia.

Bayesian Models for Integrative Genomics

Advances in Statistical Bioinformatics: Models and Integrative Inference for High- Throughput Data, Kim-Anh Do, Zhaohui Steve Qin and Marina Vannucci (Eds). Cambridge University Press, 272-291.

Bayesian methods have found many successful applications in genomics. Methods that employ variable selection have been particularly successful, as they allow to handle situations where the amount of measured variables can be much greater than the number of observations. Here we describe Bayesian variable selection models for integrative genomics. We first look into models that incorporate external biological information into the analysis of experimental data, in particular gene expression data. We address linear settings, including regression and classification models, and mixture models, including clustering and discriminant analysis. We then focus on Bayesian models that achieve an even greater type of integration, by incorporating into the modeling experimental data from different platforms, together with prior knowledge. We look in particular at graphical models, integrating gene expression data with microRNA expression data. All modeling settings we consider exploit variable selection techniques and utilize prior constructions that cleverly incorporate biological knowledge about structural dependencies among the variables.