NSECDA: Natural Semantic Enhancement for CircRNA-Disease Association Prediction
Author:
Affiliation:
1. Guangxi Academy of Sciences, Nanning, China
2. School of Computer Sciences, Northwestern Polytechnical University, Xi'an, China
3. Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi, China
Funder
Science and Technology Innovation 2030
National Natural Science Foundation of China
Zaozhuang University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics
Link
http://xplorestaging.ieee.org/ielx7/6221020/9910251/09860059.pdf?arnumber=9860059
Reference34 articles.
1. An efficient approach based on multi-sources information to predict circRNA–disease associations using deep convolutional neural network
2. DWNN-RLS: regularized least squares method for predicting circRNA-disease associations
3. PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations
4. GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm
5. Prediction of CircRNA-Disease Associations Using KATZ Model Based on Heterogeneous Networks
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