Bayesian ensemble methods for survival prediction in gene expression data

Bayesian ensemble methods for survival prediction in gene expression data

MOTIVATION We propose a Bayesian ensemble method for survival prediction in high-dimensional gene expression data. We specify a fully Bayesian hierarchical approach based on an ensemble 'sum-of-trees' model and illustrate our method using three popular survival models. Our non-parametric method incorporates both additive and interaction effects between genes, which results in high predictive ac...

Prediction of blood cancer using leukemia gene expression data and sparsity-based gene selection methods

Background: DNA microarray is a useful technology that simultaneously assesses the expression of thousands of genes. It can be utilized for the detection of cancer types and cancer biomarkers. This study aimed to predict blood cancer using leukemia gene expression data and a robust ℓ2,p-norm sparsity-based gene selection method. Materials and Methods: In this descriptive study, the microarray ...

Ensemble Methods for MiRNA Target Prediction from Expression Data

BACKGROUND microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each ...

Bayesian Prediction in Spatial Data Analysis

Methods for class prediction with high-dimensional gene expression data

An increasing amount of genomic data has become available. The work deals with class prediction with highdimensional gene expression data. Combining gene expression data with other data can improve the prediction of disease prognosis. The main part of the work is aimed at combining gene expression data with clinical data. We use logistic regression models that can be built through various regul...