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VAE Trainer

This page allows you to train a VAE model using HT-SELEX data.

Accessing the VAE Trainer Page

Navigate to the VAE Trainer by clicking the VAE Trainer link in the top menu or the navigation bar.

Accessing the VAE Trainer page

Training Process

Initiating a New Training Job

Click the + Add a New Training Job button located at the top left of the page.

Location of the button for adding training job for VAE

Setting the parameters

Set the preprocessing parameters for your training job, then click the Next button.

Configuration page for preprocessing parameters

Preprocessing Parameters

  1. Model Type: The type of the model. Choose RaptGen.
  2. Experiment Name: Assign a name to your experiment.
  3. Target Length: Specify the sequence length, including adapter length. The Estimate button activates after loading SELEX sequences (⑦).
  4. Adapters: Set sequence adapters (constant region at 3' or 5' end). The Estimate button becomes available after setting the Target Length parameter (③).
  5. Filtering Tolernace: Set the tolerance for sequence filtering, which is the allowable difference between the target length and the length of the sequence.
  6. Minimum Count: Define the minimum sequence count. Sequences below this threshold will be filtered out.
  7. SELEX sequences: Upload your training sequences in .fasta or .fastq format.

Next, set the training parameters and click the Train button.

Configuration page for training parameters

Training Parameters

  1. Reiteration of Training: Specify how many times to repeat training with the same dataset. (Not epoch, but the number of training)
  2. Device: Select CPU or cuda:X for GPU (if available).
  3. Seed Value: Set a seed for reproducibility. Generate Random Seed button in the right will generate a random seed.
  4. Maximum Number of Epochs: Set the maximum training epochs.
  5. Early Stopping Patience: Define how many epochs without improvement of the validation loss before stopping the training.
  6. Beta Weighting Epochs: Specify epochs for increasing beta value from 0 to 1.
  7. Force Matching Epochs: Specify epochs for force-matching. During the force-matching phase, the profile HMM model will be forced to have less penalty on match-to-match state transition score.
  8. Match Cost: Define the intensity of the force-matching. The larger value will force the profile HMM model to have less penalty match-to-match state transition score.
  9. pHMM model length: Set the profile HMM model length. Default is random region length.

Training the model

After submission, the job appears in the Running Jobs list. You can monitor progress, stop, delete, or rename jobs from this list.

Overview of detail page of job in progress

  • To stop, delete or rename the job, you can click the Stop, Delete, or Rename button, respectively.
  • The current status of the experiment section shows the job's progress and total number of jobs. current status of the experiment section
  • The Training Parameters section displays the job's training parameters.
    training parameters
  • The Job information section provides details about the training job.
    job information

Adding the Trained Model to the Model List

Once training completes, add the model to the model list by clicking Add to Viewer Dataset button.

Button to add training job to the dataset

Name your model in the pop-up modal and click the Add to Viewer Dataset button.

Modal for adding job to the dataset

The trained model will then be available in the Viewer, GMM Trainer and Bayes Optimization pages.

Next Step

Proceed to the GMM Trainer page to train a GMM model.

If you have already trained a GMM model or have a initializing dataset in BO module, you can proceed to the Bayesian Optimization page.