Prediction of miRNA-disease association based on multisource inductive matrix completion

• Published in: Scientific reports Vol. 14; no. 1; pp. 27503 - 15
• Main Authors: Wang, YaWei, Yin, ZhiXiang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11.11.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/2397; Ablation experiment; Algorithms; Autoencoder; Computational Biology - methods; Disease; Genetic Predisposition to Disease; Humanities and Social Sciences; Humans; Inductive matrix completion; MicroRNAs; MicroRNAs - genetics; miRNA; miRNA-disease association; Multi-source information; multidisciplinary; Nucleotides; Ontology; Optimization algorithm; Predictions; Science; Science (multidisciplinary); Ablation experiment; Inductive matrix completion; Multi-source information; Optimization algorithm; miRNA-disease association; Autoencoder
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-78212-w

MicroRNAs (miRNAs) are endogenous non-coding RNAs approximately 23 nucleotides in length, playing significant roles in various cellular processes. Numerous studies have shown that miRNAs are involved in the regulation of many human diseases. Accurate prediction of miRNA-disease associations is crucial for early diagnosis, treatment, and prognosis assessment of diseases. In this paper, we propose the Autoencoder Inductive Matrix Completion (AEIMC) model to identify potential miRNA-disease associations. The model captures interaction features from multiple similarity networks, including miRNA functional similarity, miRNA sequence similarity, disease semantic similarity, disease ontology similarity, and Gaussian interaction kernel similarity between miRNAs and diseases. Autoencoders are used to extract more complex and abstract data representations, which are then input into the inductive matrix completion model for association prediction. The effectiveness of the model is validated through cross-validation, stratified threshold evaluation, and case studies, while ablation experiments further confirm the necessity of introducing sequence and ontology similarities for the first time.