A balanced iterative random forest for gene selection from microarray data.

ABSTRACT:

Background

The wealth of gene expression values being generated by high throughput microarray technologies leads to complex high dimensional datasets. Moreover, many cohorts have the problem of imbalanced classes where the number of patients belonging to each class is not the same. With this kind of dataset, biologists need to identify a small number of informative genes that can be used as biomarkers for a disease.

Results

This paper introduces a Balanced Iterative Random Forest (BIRF) algorithm to select the most relevant genes for a disease from imbalanced high-throughput gene expression microarray data. Balanced iterative random forest is applied on four cancer microarray datasets: a childhood leukaemia dataset, which represents the main target of this paper, collected f

SUBMITTER: Anaissi A 

PROVIDER: S-EPMC3766035 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature