Computational approaches for circRNA-disease association prediction:a review  

作　　者：Mengting NIU Yaojia CHEN Chunyu WANG Quan ZOU Lei XU 

机构地区：[1]Center for Informational Biology,University of Electronic Science and Technology of China,Chengdu 610054,China [2]School of Applied Chemistry and Biological Technology,Shenzhen Polytechnic University,Shenzhen 518055,China [3]Institute of Fundamental and Frontier Sciences,University of Electronic Science and Technology of China,Chengdu 610054,China [4]Yangtze Delta Region Institute(Quzhou),University of Electronic Science and Technology of China,Quzhou 324000,China [5]Faculty of Computing,Harbin Institute of Technology,Harbin 150006,China

出　　处：《Frontiers of Computer Science》2025年第4期99-113,共15页计算机科学前沿(英文版)

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.62231013,62201129,62303328,62302341,62271329,62372332);the National Key R&D Program of China(2022ZD0117700);the National funded postdoctoral researcher program of China(GZC20230382);the Shenzhen Polytechnic University Research Fund(6024310027K,6022310036K,6023310037K);the Key Field of Department of Education of Guangdong Province(2022ZDZX2082);the Special Science Foundation of Quzhou(2023D036).

摘　　要：Circular RNA(circRNA)is a covalently closed RNA molecule formed by back splicing.The role of circRNAs in posttranscriptional gene regulation provides new insights into several types of cancer and neurological diseases.CircRNAs are associated with multiple diseases and are emerging biomarkers in cancer diagnosis and treatment.The associations prediction is one of the current research hotspots in the field of bioinformatics.Although research on circRNAs has made great progress,the traditional biological method of verifying circRNA-disease associations is still a great challenge because it is a difficult task and requires much time.Fortunately,advances in computational methods have made considerable progress in circRNA research.This review comprehensively discussed the functions and databases related to circRNA,and then focused on summarizing the calculation model of related predictions,detailed the mainstream algorithm into 4 categories,and analyzed the advantages and limitations of the 4 categories.This not only helps researchers to have overall understanding of circRNA,but also helps researchers have a detailed understanding of the past algorithms,guide new research directions and research purposes to solve the shortcomings of previous research.

关 键 词：circular RNA disease association prediction machine learning data mining deep learning 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]