Cprisp framework predicts circRNA-RBP interactions for neurodegeneration research
Researchers have developed CPRISP, a deep learning framework designed to predict interactions between circular RNAs (circRNAs) and RNA-binding proteins (RBPs). The model integrates multiple data types, including transcript sequence composition, physicochemical properties, secondary structure, and protein features, to improve prediction accuracy. In testing, CPRISP outperformed existing methods…
Key points
- CPRISP predicts circRNA-RBP interactions across 37 benchmark datasets with improved accuracy.
- Model identifies pathways in Alzheimer’s, Parkinson’s, and ALS proteins for experimental follow-up.
- Framework combines sequence, structure, and protein features for interpretable predictions.
The study applied the framework to proteins associated with Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis (ALS). Analysis revealed recurrent biological pathways involving nucleocytoplasmic transport, lysosomal signaling, and guanosine triphosphatase activity. The model also prioritized specific compounds for further experimental follow-up. However, the authors explicitly state that these findings support accurate mapping of interactions and do not imply therapeutic efficacy.
The work was supported by the National Natural Science Foundation of China and the Guangdong Basic and Applied Basic Research Foundation. The article is published under a Creative Commons Attribution 4.0 International License, allowing for broad sharing and adaptation with proper credit.
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