The study of RNA modifications has become a critical area of research in molecular biology, revealing the complex regulatory mechanisms that control gene expression and cellular function. Nanopore sequencing, a high-throughput technology, has emerged as a promising tool for detecting and profiling RNA modifications. Transfer learning, a machine learning technique, has shown potential in improving the accuracy of identifying diverse RNA modifications from nanopore sequencing data.
