# OmiVAE

***Please check the updated version of OmiVAE:***

[OmiEmbed](https://github.com/zhangxiaoyu11/OmiEmbed)

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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](https://arxiv.org/abs/1908.06278)

## Citation

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

```bibtex

@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](https://github.com/zhangxiaoyu11/OmiEmbed)

## License

This source code is licensed under the [MIT](https://github.com/zhangxiaoyu11/OmiVAE/blob/master/LICENSE) license.