A random forest based computational model for predicting novel lncRNA-disease associations

• Main Authors: Yao, Dengju, Zhan, Xiaojuan, Zhan, Xiaorong, Kwoh, Chee Keong, Li, Peng, Wang, Jinke
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering; Random Forest; Variable Importance
• Online Access: https://hdl.handle.net/10356/146950
• Institution: Nanyang Technological University

Background: Accumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA...