DeepMiRBP: a hybrid model for predicting microRNA-protein interactions based on transfer learning and cosine similarity
Our proposed DeepMiRBP strategy represents the first of its kind designed for microRNA-protein interaction prediction. Its promising performance underscores the model's potential to uncover novel interactions critical for small RNA sorting and packaging, as well as to infer new RNA transporter proteins. The methodologies and insights from DeepMiRBP offer a scalable template for future small RNA research, from mechanistic discovery to modeling disease-related cell-to-cell communication, emphasizing its adaptability and potential for developing novel small RNA-centric therapeutic interventions and personalized medicine.
Authors
- Juan Cui (ORCID: https://orcid.org/0000-0001-6581-6850)
- Sasan Azizian
Institutions
- University of Nebraska–Lincoln (US)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12859-024-05985-2
- Citations
- 2
- Primary Topic
- MicroRNA in disease regulation
- Type
- article
- Field-Weighted Citation Impact
- 0.57