i5hmCVec: Identifying 5-Hydroxymethylcytosine Sites of Drosophila RNA Using Sequence Feature Embeddings - Liu, Hang-Yu; Du, Pu-Feng (Frontiers in Genetics, 2022)
As one of the most important RNA modifications, 5hmC (5-hydroxymethylcytosine) plays an important role in many biological processes. Accurately identifying RNA modification sites helps understand the function of RNA modification. In this work, we propose a computational method for identifying the 5hmC modified regions using machine learning algorithms. We applied a sequence feature embedding method based on the dna2vec algorithm to represent the RNA sequence. The results showed that the performance of our model is better than state-of-art methods. All dataset and source codes used in this study is available at: https://github.com/liu-h-y/5hmC_model.