SCPLPA: An miRNA–disease association prediction model based on spatial consistency projection and label propagation algorithm

• Published in: Journal of cellular and molecular medicine Vol. 28; no. 9; pp. e18345 - n/a
• Main Authors: Chen, Min, Deng, Yingwei, Li, Zejun, Ye, Yifan, Zeng, Lijun, He, Ziyi, Peng, Guofang
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.05.2024; John Wiley and Sons Inc
• Subjects: Algorithms; Computational Biology - methods; Computer applications; Decision trees; Deep learning; Disease; Gene Regulatory Networks; Genetic Predisposition to Disease; Humans; label propagation; Machine learning; Medical research; Methods; MicroRNAs; MicroRNAs - genetics; miRNA; miRNA–disease association prediction; Original; Prediction models; Semantics; spatial consistency projection; Support vector machines; label propagation; spatial consistency projection; miRNA–disease association prediction
• ISSN: 1582-1838; 1582-4934
• DOI: 10.1111/jcmm.18345

Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA–disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA–disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.