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
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011344

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 learning (DL) based methods have been proposed for predicting circRNA-disease association and achieved impressive prediction performance. However, there are two main drawbacks to these methods. The first is these methods underutilize biometric information in the data. Second, the features extracted by these methods are not outstanding to represent association characteristics between circRNAs and diseases. In this study, we developed a novel deep learning model, named iCircDA-NEAE, to predict circRNA-disease associations. In particular, we use disease semantic similarity, Gaussian interaction profile kernel, circRNA expression profile similarity, and Jaccard similarity simultaneously for the first time, and extract hidden features based on accelerated attribute network embedding (AANE) and dynamic convolutional autoencoder (DCAE). Experimental results on the circR2Disease dataset show that iCircDA-NEAE outperforms other competing methods significantly. Besides, 16 of the top 20 circRNA-disease pairs with the highest prediction scores were validated by relevant literature. Furthermore, we observe that iCircDA-NEAE can effectively predict new potential circRNA-disease associations.