MSPCD: predicting circRNA-disease associations via integrating multi-source data and hierarchical neural network

• Published in: BMC bioinformatics Vol. 23; no. Suppl 8; pp. 1 - 427
• Main Authors: Deng, Lei, Liu, Dayun, Li, Yizhan, Wang, Runqi, Liu, Junyi, Zhang, Jiaxuan, Liu, Hui
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 14.10.2022; BioMed Central; BMC
• Subjects: Accuracy; Arthritis; Case studies; Circrna-disease associations; Computational biology; Computer applications; Data mining; Datasets; Disease; Experiments; Feature extraction; Gene expression; Health aspects; High-order features; Machine learning; Medical genetics; Medical research; Medical treatment; Medicine, Experimental; Methods; miRNA; Multi-source data; Neural network; Neural networks; Ontology; RNA; Semantics; Similarity; China
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-022-04976-5

Increasing evidence shows that circRNA plays an essential regulatory role in diseases through interactions with disease-related miRNAs. Identifying circRNA-disease associations is of great significance to precise diagnosis and treatment of diseases. However, the traditional biological experiment is usually time-consuming and expensive. Hence, it is necessary to develop a computational framework to infer unknown associations between circRNA and disease. In this work, we propose an efficient framework called MSPCD to infer unknown circRNA-disease associations. To obtain circRNA similarity and disease similarity accurately, MSPCD first integrates more biological information such as circRNA-miRNA associations, circRNA-gene ontology associations, then extracts circRNA and disease high-order features by the neural network. Finally, MSPCD employs DNN to predict unknown circRNA-disease associations. Experiment results show that MSPCD achieves a significantly more accurate performance compared with previous state-of-the-art methods on the circFunBase dataset. The case study also demonstrates that MSPCD is a promising tool that can effectively infer unknown circRNA-disease associations.