{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang Z"],"funding":["Japan Society for the Promotion of Science"],"pagination":["139"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10082986"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(1)"],"pubmed_abstract":["<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 pro"],"journal":["BMC bioinformatics"],"pubmed_title":["Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data."],"pmcid":["PMC10082986"],"funding_grant_id":["20H05967"],"pubmed_authors":["Takagi T","Tian YS","Zhou Y","Wang Z","Song J","Shibuya T"],"additional_accession":[]},"is_claimable":false,"name":"Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data.","description":"<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 pro","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Apr","modification":"2025-04-22T11:57:26.076Z","creation":"2025-02-19T01:31:57.045Z"},"accession":"S-EPMC10082986","cross_references":{"pubmed":["37031189"],"doi":["10.1186/s12859-023-05267-3"]}}