Cheng Han, Liang Mengyu, Gao Yiwen, Zhao Wenshan, Guo Wei-Feng
School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China.
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
Genes (Basel). 2025 Feb 20;16(3):244. doi: 10.3390/genes16030244.
It is important to identify disease biomarkers (DBs) for early diagnosis and treatment of complex diseases in personalized medicine. However, existing methods integrating intelligence technologies and multiomics to predict key biomarkers are limited by the complex dynamic characteristics of omics data, making it difficult to meet the high-precision requirements for biomarker characterization in large dimensions. This study reviewed current analysis methods of evolutionary computation (EC) by considering the essential characteristics of DB identification problems and the advantages of EC, aiming to explore the complex dynamic characteristics of multiomics. In this study, EC-based biomarker identification strategies were summarized as evolutionary algorithms, swarm intelligence and other EC methods for molecular and module DB identification, respectively. Finally, we pointed out the challenges in current research and future research directions. This study can enrich the application of EC theory and promote interdisciplinary integration between EC and bioinformatics.
在个性化医疗中，识别疾病生物标志物（DBs）对于复杂疾病的早期诊断和治疗至关重要。然而，现有的将智能技术与多组学相结合以预测关键生物标志物的方法受到组学数据复杂动态特征的限制，难以满足大维度生物标志物表征的高精度要求。本研究通过考虑DB识别问题的本质特征和进化计算（EC）的优势，回顾了当前的进化计算分析方法，旨在探索多组学的复杂动态特征。在本研究中，基于EC的生物标志物识别策略分别总结为用于分子和模块DB识别的进化算法、群体智能及其他EC方法。最后，我们指出了当前研究中的挑战和未来的研究方向。本研究可以丰富EC理论的应用，并促进EC与生物信息学之间的跨学科整合。
