Inference on unseen data related to Anti-cancer Peptides:

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 Anti-cancer peptides sequences.
Users can also input Anti-cancer peptides sequence.
Input file must contains only Anti-cancer peptides sequences.
User must have to select one Anti-cancer peptides type.
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

Training the Model from Scratch

Users need to provide a csv file containing Anticancer peptides data.
User has the freedom to choose value of K-tuple (K-mer).
User has the freedom to choose data split method.
User has the freedom to choose number of folds for data split.
User has 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.