- 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.
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