ArticleBMC bioinformatics2022
MSPCD: predicting circRNA-disease associations via integrating multi-source data and hierarchical neural network.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data.PLoS computational biology · 2025Article
- CMAGN: circRNA-miRNA association prediction based on graph attention auto-encoder and network consistency projection.BMC bioinformatics · 2024Article
- Biolinguistic graph fusion model for circRNA-miRNA association prediction.Briefings in bioinformatics · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundIncreasing 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.
resultsIn 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.
conclusionsExperiment 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.
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