Feature Selection of OMIC Data by Ensemble Swarm Intelligence Based Approaches - Yao, Zhaomin; Zhu, Gancheng; Too, Jingwei; Duan, Meiyu; Wang, Zhiguo (Frontiers in Genetics, 2022)
OMIC datasets contain high-dimensional data and the correlation with OMIC features is very complicated. It is difficult to establish linkage between these characteristics and certain biological traits of significance. The proposed ensemble swarm intelligence-based approaches can identify key biomarkers and efficiently reduce the dimension of features. It is an end-to-end method that only relies on the rules of the algorithm itself, without presets such as the number of filtering features. Additionally, this method achieves good classification accuracy without excessive consumption of computing resources.