Training Mode

  • Users need to provide a csv file containing RNA sequences and multi-compartment subcellular localization information.
  • They have the freedom to choose target RNA class and specie.
  • They have the freedom to choose Kmer, stride size, depth of context, and embedding dimension for k-hop neigbourhood based statistical representation learning approach.
  • They have the freedom to choose batch size, learning rate, weight decay, dropout, and number of epochs to train deep learning model from scratch.
  • Sign up preferably using organizational email account with providing the required data and purpose of experimentation. After the completion of SignUp process, one need to wait for approval of account and permission for training
  • If the request is approved, you will be able to login just for one time training. On successful activation of processing command, exploratory training engine will process the data shortly in order to train the model.
  • At the end of training, users can download performance related artifacts to analyze the deep learning model behavior.

Prediction Mode:

  • User need to provide novel RNA sequences in given textbox.
  • They have to select specie specific target RNA class pre-trained deep learning model to perform inference on novel RNA sequences for RNA associated multi-compartment subcellular localization prediction.
  • At the end of inference, user can visualize the predicted class.