文献类型：期刊文献

英文题名：Deep learning models for disease-associated circRNA prediction: a review

作者：Chen, Yaojia[1,2];Wang, Jiacheng[2];Wang, Chuyu[3];Liu, Mingxin[1];Zou, Quan[4,5,6]

机构：[1]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang, Peoples R China;[2]Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu, Peoples R China;[3]Harbin Inst Technol, Fac Comp, Harbin, Peoples R China;[4]Univ Elect Sci & Technol China, Chengdu, Sichuan, Peoples R China;[5]Inst Elect & Elect Engineers IEEE, Piscataway, NJ 08854 USA;[6]Assoc Comp Machinery ACM, New York, NY 10036 USA

年份：2022

卷号：23

期号：6

外文期刊名：BRIEFINGS IN BIOINFORMATICS

收录：SCI-EXPANDED(收录号：WOS:000855883000001)、、Scopus(收录号：2-s2.0-85142403700)、WOS

基金：National Natural Science Foundation of China (No. 62131004, No. 61922020); Sichuan Provincial Science Fund for Distinguished Young Scholars (2021JDJQ0025); Municipal Government of Quzhou(2020D003,2021D004).

语种：英文

外文关键词：circular RNA; circRNA-disease associations; deep learning; database; neural networks

外文摘要：Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep learning with a more powerful ability for representation learning enables it to be a promising technology for predicting disease-associated circRNAs. In this review, we mainly introduce the most popular databases related to circRNA, and summarize three types of deep learning-based circRNA-disease associations prediction methods: feature-generation-based, type-discrimination and hybrid-based methods. We further evaluate seven representative models on benchmark with ground truth for both balance and imbalance classification tasks. In addition, we discuss the advantages and limitations of each type of method and highlight suggested applications for future research.