2. Training Graph-Transformer

[03-134202-096]

Kindly explain these text files in the Training Graph-Transformer and their format, from where i get these text files?
Split training, validation, and testing dataset and store them in text files as:

sample1 \t label1
sample2 \t label2
LUAD/C3N-00293-23 \t luad
...

[GSWS]

The text files mentioned are used to store the data samples and their corresponding labels in a tab-separated format. Each line in the file represents a single sample, where:

• sample1: Refers to the identifier or path to a sample (e.g., file name, image ID, or dataset ID).
• label1: Indicates the label assigned to that sample (e.g., cancer, normal, or specific classes like luad for lung adenocarcinoma).

The format is as follows:
sample1 \t label1
sample2 \t label2
LUAD/C3N-00293-23 \t luad
...

How to Create These Text Files:

1. Source the Data: Labels such as cancer or normal can often be obtained from public datasets like the GDC Data Portal or other datasets appropriate for your task.

2. Split the Dataset:

• Training Set: Used to train the model.
• Validation Set: Used to monitor model performance during training and prevent overfitting.
• Test Set: Used to evaluate the final model's performance on unseen data.
  Ensure that these splits are mutually exclusive to maintain the integrity of the evaluation.

3. Prepare the Files:
   For each split (training, validation, and testing), create a text file where each line contains a sample and its label, separated by a tab (\t).

Example Workflow:

• Download the dataset from a public source (e.g., GDC).
• Parse the dataset to extract the sample identifiers and labels.
• Randomly split the dataset into training, validation, and testing sets.
• Write the splits into separate text files (e.g., train.txt, val.txt, test.txt) in the format specified.

Example:
For a dataset with samples categorized into cancer and normal, the files might look like:

train.txt:
sample1 \t cancer
sample2 \t normal
sample3 \t cancer

val.txt:
sample4 \t normal
sample5 \t cancer

test.txt:
sample6 \t normal
sample7 \t cancer

These files can then be fed into a Graph-Transformer training pipeline, where the model uses the training set to learn, the validation set to tune hyperparameters, and the test set to evaluate its performance.