SUNY Poly Assistant Professor of Computer Science Dr. Amirhossein Manzourolajdad (pictured on the right), in collaboration with Dr. Mohammad Mohebbi at the University of North Georgia, has published new research on microRNA (miRNA) target site detection, introducing a Multi-Input Neural Network (MINN) model that significantly improves computational predictions of miRNA interactions. The study, titled “A Multi-Input Neural Network Model for Accurate MicroRNA Target Site Detection,” was published in Non-Coding RNA and presents major advancements for genetic research, RNA-based therapies, and precision medicine.

miRNAs are small RNA molecules that regulate gene expression and play a key role in diseases such as cancer and neurodegenerative disorders. Identifying their target sites has been a challenge due to the limitations of traditional lab and computational methods. This research introduces an AI-driven approach that integrates RNA structure, binding probabilities, and thermodynamics into a deep learning model, achieving higher accuracy than existing methods like TargetScan and miRanda.

The study demonstrates that MINN can more precisely predict miRNA target sites, paving the way for better drug development, gene therapy, and biomarker discovery. 

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