[im6Am-DC: a model for predicting RNA sequence N6,2'-O-dimethyladenosine modification by integrating DenseNet and...

N6,2'-O-dimethyladenosine (m6Am), as a critical RNA epigenetic modification, has become a hot topic in current research due to its unique chemical structure and biological functions. m6Am not only regulates mRNA stability and half-life but also modulates its interactions with translation initiation factors and RNA-binding proteins, thereby playing a crucial role in post-transcriptional regulation. Recent medical research indicates that m6Am is closely associated with metabolic disorders such as obesity and type 2 diabetes mellitus, as well as multiple viral infections and tumor development. Therefore, predicting m6Am site holds significant importance for early diagnosis and precision treatment of diseases. Although the biological functions of m6Am modification are gradually attracting attention, related research is still limited by the limitations of detection technology and high costs. Computational biology methods provide new ideas for large-scale identification of m6Am sites. This paper proposes an imbalanced m6Am site prediction model-im6Am-DC. The model employs a DenseNet architecture to extract high-level local features and introduces a CA attention mechanism to highlight crucial feature information. Furthermore, to solve the data imbalance, the model utilizes the focal loss function. Experimental results showed that on the full transcript dataset, the im6Am-DC model achieved the sensitivity (Sn) of 0.523 7, specificity (Sp) of 0.988 4, accuracy (Acc) of 0.946 1, and Matthews correlation coefficient (MCC) of 0.629 8. On the mature RNA dataset, the model demonstrated equally strong performance across these metrics. Simultaneously, we employed the im6Am-DC model to predict across multiple distinct datasets, including unbalanced m6Am site datasets, balanced m6Am site datasets, and other types of RNA modification sites. This comprehensive, multidimensional approach enabled us to evaluate the performance and application potential of the model. The results demonstrate that the im6Am-DC model exhibits significant advantages in predicting unbalanced m6Am sites, providing effective technical support for studying the roles of m6Am modifications and exploring related disease mechanisms.