TSMDA: Target and symptom-based computational model for miRNA-disease-association prediction

• Published in: Molecular therapy. Nucleic acids 2021-12, Vol.26, p.536-546
• Main Authors: Uthayopas, Korawich, de Sá, Alex G.C., Alavi, Azadeh, Pires, Douglas E.V., Ascher, David B.
• Format: Article
• Language: English
• Subjects: cancer; disease; machine learning; microRNA; miRNA-disease association prediction; miRNA-target interaction; Original; symptom-based similarity; target-based similarity; XGBoost
• ISSN: 2162-2531; 2162-2531
• DOI: 10.1016/j.omtn.2021.08.016

The emergence of high-throughput sequencing techniques has revealed a primary role of microRNAs (miRNAs) in a wide range of diseases, including cancers and neurodegenerative disorders. Understanding novel relationships between miRNAs and diseases can potentially unveil complex pathogenesis mechanisms, leading to effective diagnosis and treatment. The investigation of novel miRNA-disease associations, however, is currently costly and time consuming. Over the years, several computational models have been proposed to prioritize potential miRNA-disease associations, but with limited usability or predictive capability. In order to fill this gap, we introduce TSMDA, a novel machine-learning method that leverages target and symptom information and negative sample selection to predict miRNA-disease association. TSMDA significantly outperforms similar methods, achieving an area under the receiver operating characteristic (ROC) curve (AUC) of 0.989 and 0.982 under 5-fold cross-validation and blind test, respectively. We also demonstrate the capability of the method to uncover potential miRNA-disease associations in breast, prostate, and lung cancers, as case studies. We believe TSMDA will be an invaluable tool for the community to explore and prioritize potentially new miRNA-disease associations for further experimental characterization. The method was made available as a freely accessible and user-friendly web interface at http://biosig.unimelb.edu.au/tsmda/. [Display omitted] This work proposes a novel model, TSMDA, for predicting miRNA-disease association using miRNA-target and disease-symptom information. TSMDA also encompasses two reliable negative sample selections to effectively predict miRNA-disease associations. TSMDA is freely available as a user-friendly web server for the community to explore potential associations for further experimental miRNA characterization.