biostudies-literatureWang ZJapan Society for the Promotion of Science139https://www.ebi.ac.uk/biostudies/studies/S-EPMC10082986biostudies-literatureUnknown24(1)<h4>Background</h4>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.<h4>Results</h4>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 proBMC bioinformaticsGenetic algorithm-based feature selection with manifold learning for cancer classification using microarray data.PMC1008298620H05967Takagi TTian YSZhou YWang ZSong JShibuya TfalseGenetic algorithm-based feature selection with manifold learning for cancer classification using microarray data.<h4>Background</h4>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.<h4>Results</h4>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 pro2023-01-01T00:00:00Z2023 Apr2025-04-22T11:57:26.076Z2025-02-19T01:31:57.045ZS-EPMC100829863703118910.1186/s12859-023-05267-3