AdmirePred: A method for predicting abundant miRNAs in Exosomes

Non-invasive disease diagnosis is a key application of blood exosomes in liquid biopsy, as they carry diverse biological molecules, including microRNAs (miRNAs) derived from their parent cells. Developing miRNA-based disease biomarkers requires the prediction of highly abundant miRNAs in exosomes under normal conditions for establishing a baseline for understanding their physiological roles and disease-specific variations. In this study, we present models for predicting highly abundant miRNAs in exosomes from their nucleotide sequences. The models were trained, tested, and evaluated on a dataset comprising 348 abundant and 349 non-abundant miRNAs. Initially, we applied alignment-based approaches, such as motif and similarity searches, but these methods yielded poor coverage. We then explored alignment-free approaches, particularly machine learning models leveraging a broad range of features. Our Extra Trees classifier, developed using binary profiles and TF-IDF features, achieved the highest performance with an AUC of 0.77. To further enhance predictive accuracy, we developed a hybrid method that combines machine learning models with alignment-based approaches, achieving an AUC of 0.854 on an independent dataset. To support research in non-invasive diagnostics and therapeutics, we have developed a web server, standalone tool, and Python package for AdmirePred, available at https://webs.iiitd.edu.in/raghava/admirepred/.