TEMINET is a novel multi-omics integration approach designed to enhance diagnostic predictions for complex human diseases by leveraging an intra-omics co-informative representation method and a trustworthy learning strategy for inter-omics fusion.
- Python 3.6
- Pytorch 1.10.2
- pytorch geometric
- sklearn
- numpy
The data used can be obtained through https://github.com/txWang/MOGONET. In our study, data from three omics were merged into one file "data.pt". You can get the classification result by running model_test.py.
This tool is for research purpose and not approved for clinical use.
This tool is developed in Yao Lab.
The copyright holder for this project is Yao Lab.
All rights reserved.