计算机科学
水准点(测量)
深度学习
人工智能
机器学习
特征(语言学)
代表(政治)
特征学习
语言学
哲学
大地测量学
政治
政治学
法学
地理
作者
Yaojia Chen,Jiacheng Wang,Chuyu Wang,Mingxin Liu,Quan Zou
摘要
Abstract 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI