Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data.

ABSTRACT:

Background

Microarray data have been widely utilized for cancer classification. The main characteristic of microarray data is "large p and small n" in that data contain a small number of subjects but a large number of genes. It may affect the validity of the classification. Thus, there is a pressing demand of techniques able to select genes relevant to cancer classification.

Results

This study proposed a novel feature (gene) selection method, Iso-GA, for cancer classification. Iso-GA hybrids the manifold learning algorithm, Isomap, in the genetic algorithm (GA) to account for the latent nonlinear structure of the gene expression in the microarray data. The Davies-Bouldin index is adopted to evaluate the candidate solutions in Isomap and to avoid the classifier dependency pro

SUBMITTER: Wang Z 

PROVIDER: S-EPMC10082986 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature