PRIMITI: A computational approach for accurate prediction of miRNA-target mRNA interaction

• Published in: Computational and structural biotechnology journal 2024-12, Vol.23, p.3030-3039
• Main Authors: Uthayopas, Korawich, de Sá, Alex G.C., Alavi, Azadeh, Pires, Douglas E.V., Ascher, David B.
• Format: Article
• Language: English
• Subjects: EXtreme Gradient Boosting (XGBoost); Machine learning; MicroRNAs; MiRNA-mediated repression; MiRNA-target interaction prediction
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2024.06.030

Current medical research has been demonstrating the roles of miRNAs in a variety of cellular mechanisms, lending credence to the association between miRNA dysregulation and multiple diseases. Understanding the mechanisms of miRNA is critical for developing effective diagnostic and therapeutic strategies. miRNA-mRNA interactions emerge as the most important mechanism to be understood despite their experimental validation constraints. Accordingly, several computational models have been developed to predict miRNA-mRNA interactions, albeit presenting limited predictive capabilities, poor characterisation of miRNA-mRNA interactions, and low usability. To address these drawbacks, we developed PRIMITI, a PRedictive model for the Identification of novel miRNA-Target mRNA Interactions. PRIMITI is a novel machine learning model that utilises CLIP-seq and expression data to characterise functional target sites in 3’-untranslated regions (3’-UTRs) and predict miRNA-target mRNA repression activity. The model was trained using a reliable negative sample selection approach and the robust extreme gradient boosting (XGBoost) model, which was coupled with newly introduced features, including sequence and genetic variation information. PRIMITI achieved an area under the receiver operating characteristic (ROC) curve (AUC) up to 0.96 for a prediction of functional miRNA-target site binding and 0.96 for a prediction of miRNA-target mRNA repression activity on cross-validation and an independent blind test. Additionally, the model outperformed state-of-the-art methods in recovering miRNA-target repressions in an unseen microarray dataset and in a collection of validated miRNA-mRNA interactions, highlighting its utility for preliminary screening. PRIMITI is available on a reliable, scalable, and user-friendly web server at https://biosig.lab.uq.edu.au/primiti. [Display omitted] •miRNAs play an essential role in post-transcriptional gene regulation through complementary base pair binding with mRNA target sites.•PRIMITI yields a new machine learning model (ML) model to identify miRNA-target mRNA interactions.•PRIMITI incorporates novelties in the characterisation of functional miRNA binding sites and also in providing more reliable training sets for its respective ML model.•PRIMITI achieved great predictive performances under cross-validation, blind test, and independent test sets, indicating its robustness in identifying miRNA-target mRNA repression.•PRIMITI was made available as