MoSwformer, a framework for cancer subtype classification, integrating multi-omics data with transfer models, MAL to tackle high dimensionality and complexity. The model leverages attention mechanisms for optimal data weighting and captures commonalities through MAL. The SET encoder enhances pattern recognition, offering a comprehensive cancer subtype analysis approach. This reposiry contains the data and python scripts in support of the ICBBE'2024 paper: MoSwformer: A Transformer-based patient classification model using multi-omics data.
The code has been tested running under Python 3.8. The required packages are as follows:
- torch == 1.12.1 (GPU version)
- numpy == 1.23.5
- pandas == 1.5.0
- scikit-learn==1.1.2
Although we build several .py files, running our code is very simple. More specifically, we only need to run train_test.py to train the model, outputting prediction results. In addition, running our code requires utilizing PyTorch's deep learning framework under Python 3.8.