A Survey on Evolutionary Computation for Identifying Biomarkers of Complex Disease

Abstract

Biological markers (i.e., biomarkers) are the key to predicting disease states and revealing the molecular mechanisms in precision medicine of complex diseases (e.g., cancer). With the advancement of high-throughput sequencing technology, there has been a significant increase in the volume and diversity of known disease omics data, where many methods have been developed to identify potential disease biomarkers (DBs) for mining the complex dynamics. As emerging artificial intelligence techniques, evolutionary computation (EC) has found extensive application in the identification of DBs, making significant achievements in mining disease omics data. However, there is currently no survey or analysis available of the existing EC methods to identify DBs on the disease omics data, resulting in missed opportunities to enhance performance and achieve successful applications in precision medicine. This article aims to present a comprehensive overview of the latest EC methods for mining the dynamics of DBs, including the summary of biomolecular omics datasets, the classification of the EC methods for DB discovery, and performance comparisons of the typical EC methods. Additionally, this article discusses challenges and potential future directions of the EC methods in the identification of DBs, providing directions and prospects for future research.