A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets.

ABSTRACT:

Objective

The objective of this investigation is to evaluate binary prediction methods for predicting disease status using high-dimensional genomic data. The central hypothesis is that the Bayesian network (BN)-based method called efficient Bayesian multivariate classifier (EBMC) will do well at this task because EBMC builds on BN-based methods that have performed well at learning epistatic interactions.

Method

We evaluate how well eight methods perform binary prediction using high-dimensional discrete genomic datasets containing epistatic interactions. The methods are as follows: naive Bayes (NB), model averaging NB (MANB), feature selection NB (FSNB), EBMC, logistic regression (LR), support vector machines (SVM), Lasso, and extreme learning machines (ELM). We use a hundred

SUBMITTER: Jiang X 

PROVIDER: S-EPMC4173174 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature