期刊论文

Zhou, Shunxian;Wang, Lei;Li, Jiechen

英文

IEEE ACCESS

2169-3536

2020

8

198415-198424

10.1109/ACCESS.2020.3035365

Corresponding authors: Shunxian Zhou (zsx_hd@hnu.edu.cn), Jiechen Li (lijiechen39555@163.com), and Lei Wang (wanglei@xtu.edu.cn) This work was supported in part by the National Natural Science Foundation of China under Grant 61873221 and Grant 61672447, in part by the Natural Science Foundation of Hunan Province under Grant 2019JJ70010, in part by the Science and Technology Plan Project of Hunan Province under Grant 2016TP1020 and Grant 2019TP1011, and in part by the Double First-Class University Project of Hunan Province (Xiangjiaotong 2018) under Grant 469.

本校为通讯机构

With rapid development of high-throughput technique in the field of life science, LncRNAs are found to be inextricably linked to many diseases that seriously endanger human health. However, traditional experiment-based methods for inferring unknown diseases-LncRNA associations are time-consuming and laborious, therefore, it has become an effective way to adopt computational models to predict potential LncRNA-disease associations in recent years. In this article, a novel prediction model called LHNHLDA has been proposed. In LHNHLDA, based on known LncRNA-disease associations downloaded from ben...