Training Mode

  • Users need to provide a csv file containing circRNA sequences and sub-cellular localization information.
  • User have the freedom to choose sequence descriptor.
  • Users also have the freedom to choose level of sequence descriptor fusion.
  • User have the freedom to choose value of K-mer (Kgap).
  • User have the freedom to choose number of folds for data split.
  • Users have the freedom to choose machine learning classifier.
  • Before starting the training process, user need to do:
  • 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 model 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 model behavior.

Prediction Mode:

Users are interested to assess the performance of novel most informative residue distribution based neural network on unseen data.

  • Users can upload a csv file of test CircRNA sequences.
  • Users can also input CircRNA sequence.
  • Input file must contains only CircRNA sequences.
  • On successful activation of processing command, exploratory data analysis engine will process the data shortly in order to predict the label against sequences.
  • User will be able to download the result file after data processing by clicking on button