Skip to content

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

License

Notifications You must be signed in to change notification settings

AiPBAND/OmiVAE

 
 

Repository files navigation

OmiVAE

Please check the updated version of OmiVAE: OmiEmbed

DOI GitHub license Safe GitHub stars GitHub forks

OmiVAE: Integrated Multi-omics Analysis Using Variational Autoencoders

Xiaoyu Zhang ([email protected])

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.

About

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

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 100.0%