GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm

• Published in: PLoS computational biology Vol. 16; no. 5; p. e1007568
• Main Authors: Wang, Lei, You, Zhu-Hong, Li, Yang-Ming, Zheng, Kai, Huang, Yu-An
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.05.2020; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Artificial neural networks; Atherosclerosis; Biology and Life Sciences; Breast cancer; Cancer; Case studies; Classifiers; Colorectal cancer; Colorectal carcinoma; Competitiveness; Computational biology; Computer applications; Datasets; Disease; Diseases; Ecology and Environmental Sciences; Engineering and Technology; Experiments; Feature extraction; Genetic aspects; Glioma; Health aspects; Machine learning; Medical treatment; Medicine and Health Sciences; Methods; MicroRNAs; Nervous system; Pathogenesis; Physical Sciences; Research and Analysis Methods; Risk factors; RNA; Similarity; Social Sciences; China; Hong Kong China

Numerous evidences indicate that Circular RNAs (circRNAs) are widely involved in the occurrence and development of diseases. Identifying the association between circRNAs and diseases plays a crucial role in exploring the pathogenesis of complex diseases and improving the diagnosis and treatment of d...