A Predictive Based Regression Algorithm for Gene Network Selection.

ABSTRACT: Gene selection has become a common task in most gene expression studies. The objective of such research is often to identify the smallest possible set of genes that can still achieve good predictive performance. To do so, many of the recently proposed classification methods require some form of dimension-reduction of the problem which finally provide a single model as an output and, in most cases, rely on the likelihood function in order to achieve variable selection. We propose a new prediction-based objective function that can be tailored to the requirements of practitioners and can be used to assess and interpret a given problem. Based on cross-validation techniques and the idea of importance sampling, our proposal scans low-dimensional models under the assumption of sparsity and, for e

SUBMITTER: Guerrier S 

PROVIDER: S-EPMC4908120 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature