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 

作者机构：School of Computer Science and Technology Beijing Institute of Technology School of Computer Science and Technology Xidian University Department of Teaching and Research Shenzhen University General Hospital Guangdong Key Laboratory for Biomedical Measurements and Ultrasound ImagingNational-Regional Key Technology Engineering Laboratory for Medical UltrasoundSchool of Biomedical Engineering Shenzhen University Medical School Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2024年第67卷第8期

页      面：341-342页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0711[理学-系统科学] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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.