Deep learning framework for RNA 5hmC prediction using RNA language model embeddings
Fig 2
Model architecture of the proposed model, InTrans-RNA5hmC.
There are two input embeddings: Word embeddings and RiNALMo embeddings. The model has two branches: The Inception branch and the Transformer branch. The Word embeddings and RiNALMo embeddings are fed to the Inception branch and the Transformer branch, respectively. The features from both branches are concatenated and passed through a feed-forward neural network for final predictions. In each three-line block, the first line represents the input tensor size, the second line represents the layer name and the third line represents the output tensor size. Batch refers to the batch size.