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

• Published in: BMC bioinformatics Vol. 24; no. 1; pp. 139 - 22
• Main Authors: Wang, Zixuan, Zhou, Yi, Takagi, Tatsuya, Song, Jiangning, Tian, Yu-Shi, Shibuya, Tetsuo
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 08.04.2023; BioMed Central; BMC
• Subjects: Accuracy; Algorithms; Analysis; Cancer; Cancer classification; Classification; Data mining; Datasets; DNA microarrays; Euclidean space; Feature selection; Gene expression; Gene Expression Profiling - methods; Gene selection; Genes; Genetic algorithm; Genetic algorithms; Genetic aspects; Genetic research; Genetic Techniques; Health aspects; Heuristic; High-throughput screening (Biochemical assaying); Humans; Hybrids; Machine learning; Manifold algorithm; Manifolds (mathematics); Methods; Microarray Analysis - methods; Microarray data; Neoplasms - classification; Neoplasms - genetics; Optimization algorithms; Performance evaluation; Probability; Australia; Manifold algorithm; Gene selection; Genetic algorithm; Cancer classification; Microarray data
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-023-05267-3

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. 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 problem. Additionally, a probability-based framework is introduced to reduce the possibility of genes being randomly selected by GA. The performance of Iso-GA was evaluated on eight benchmark microarray datasets of cancers. Iso-GA outperformed other benchmarking gene selection methods, leading to good classification accuracy with fewer critical genes selected. The proposed Iso-GA method can effectively select fewer but critical genes from microarray data to achieve competitive classification performance.