计算机科学
自编码
人工智能
特征提取
分类器(UML)
模式识别(心理学)
人工神经网络
机器学习
Boosting(机器学习)
数据挖掘
作者
Xinfei Wang,Chang-Qing Yu,Zhu‐Hong You,Liping Li,Wenzhun Huang,Zhong-Hao Ren,Yue-Chao Li,Meng-Meng Wei
摘要
A large number of studies have shown that circular RNA (circRNA) affects biological processes by competitively binding miRNA, providing a new perspective for the diagnosis, and treatment of human diseases. Therefore, exploring the potential circRNA-miRNA interactions (CMIs) is an important and urgent task at present. Although some computational methods have been tried, their performance is limited by the incompleteness of feature extraction in sparse networks and the low computational efficiency of lengthy data.In this paper, we proposed JSNDCMI, which combines the multi-structure feature extraction framework and Denoising Autoencoder (DAE) to meet the challenge of CMI prediction in sparse networks. In detail, JSNDCMI integrates functional similarity and local topological structure similarity in the CMI network through the multi-structure feature extraction framework, then forces the neural network to learn the robust representation of features through DAE and finally uses the Gradient Boosting Decision Tree classifier to predict the potential CMIs. JSNDCMI produces the best performance in the 5-fold cross-validation of all data sets. In the case study, seven of the top 10 CMIs with the highest score were verified in PubMed.The data and source code can be found at https://github.com/1axin/JSNDCMI.
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