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: London BioMed Central 08.04.2023; BioMed Central Ltd; BMC
• Subjects: Accuracy; Algorithms; Analysis; Bioinformatics; Biomedical and Life Sciences; Cancer; Cancer classification; Classification; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; 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; Life Sciences; Machine learning; Manifold algorithm; Manifolds (mathematics); Methods; Microarray Analysis - methods; Microarray data; Microarrays; 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

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 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. Conclusions The proposed Iso-GA method can effectively select fewer but critical genes from microarray data to achieve competitive classification performance.