Multiple types of disease-associated RNAs identification for disease prognosis and therapy using heterogeneous graph learning  

作　　者：Wenxiang ZHANG Hang WEI Wenjing ZHANG Hao WU Bin LIU 

机构地区：[1]School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China [2]School of Computer Science and Technology,Xidian University,Xi'an 710071,China [3]Department of Teaching and Research,Shenzhen University General Hospital,Shenzhen 518055,China [4]Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging,National-Regional Key Technology Engineering Laboratory for Medical Ultrasound,School of Biomedical Engineering,Shenzhen University Medical School,Shenzhen 518055,China [5]Advanced Research Institute of Multidisciplinary Science,Beijing Institute of Technology,Beijing 100081,China

出　　处：《Science China(Information Sciences)》2024年第8期337-338,共2页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.62325202,62372041,U22A2039).

摘　　要：Identifying disease-associated RNAs is crucial in revealing the pathogenic mechanisms of diseases[1],and biologists have made notable progress in this field[2].However,more effective computational methods are needed to provide reference disease-associated RNAs,reducing the manpower and material resources required for biological experiments.With the powerful ability of graph neural networks in detecting association patterns[3],several graph-learning-based methods are proposed to identify disease-associated RNAs.For example,HGC-GAN integrated the strengths of heterogeneous graph convolutional neural network and generative adversarial network to predict candidate disease-associated lncRNAs[4].

关 键 词：CONVOLUTION NEURAL network 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] Q811.4[自动化与计算机技术—控制科学与工程]