iCircDA-NEAE: Accelerated attribute network embedding and dynamic convolutional autoencoder for circRNA-disease associations prediction

• Published in: PLoS computational biology Vol. 19; no. 8; p. e1011344
• Main Authors: Yuan, Lin, Zhao, Jiawang, Shen, Zhen, Zhang, Qinhu, Geng, Yushui, Zheng, Chun-Hou, Huang, De-Shuang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.08.2023; Public Library of Science (PLoS)
• Subjects: Ablation; Algorithms; Analysis; Biological markers; Biology and Life Sciences; Biomarkers; Biometrics; Breast cancer; Circular RNA; Colorectal cancer; Computer and Information Sciences; Datasets; Deep learning; Disease; Embedding; Experiments; Gene expression; Genetic aspects; Graph representations; Health aspects; Humans; Machine learning; Medical diagnosis; Medical research; Medicine and Health Sciences; Medicine, Experimental; MicroRNAs; Pathogenesis; Physical Sciences; Predictions; Research and Analysis Methods; RNA; RNA, Circular - genetics; Semantics; Similarity; China

Accumulating evidence suggests that circRNAs play crucial roles in human diseases. CircRNA-disease association prediction is extremely helpful in understanding pathogenesis, diagnosis, and prevention, as well as identifying relevant biomarkers. During the past few years, a large number of deep learn...