OmiVAE

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OmiVAE: Integrated Multi-omics Analysis Using Variational Autoencoders

Xiaoyu Zhang (x.zhang18@imperial.ac.uk)

Data Science Institute, Imperial College London

Introduction

OmiVAE is an end-to-end deep learning model for low dimensional latent space extraction and multi-class classification on multi-omics datasets.

Accepted by 2019 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2019)

Paper Link: arXiv

Citation

If you use this code for your research, please cite our paper.

@inproceedings{OmiVAE2019,
  title={Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification},
  author={Zhang, Xiaoyu and Zhang, Jingqing and Sun, Kai and Yang, Xian and Dai, Chengliang and Guo, Yike},
  booktitle={Bioinformatics and Biomedicine (BIBM), 2019 IEEE International Conference on},
  year={2019}
}

OmiEmbed

Please check the updated version of OmiVAE:
OmiEmbed

License

This source code is licensed under the MIT license.