# OmiTrans: generative adversarial networks based omics-to-omics translation framework

arXiv: https://arxiv.org/abs/2111.13785 · HTML: https://mlsanity.com/paper/2111.13785/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 27 Nov 2021.

## Builds on (most cited first, 21 of 21)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks](https://mlsanity.com/paper/1711.10485/) (2017)
- [Generative Visual Manipulation on the Natural Image Manifold](https://mlsanity.com/paper/1609.03552/) (2016)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)
- [Large Scale Adversarial Representation Learning](https://mlsanity.com/paper/1907.02544/) (2019)
- [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks](https://mlsanity.com/paper/1506.05751/) (2015)
- [MirrorGAN: Learning Text-to-image Generation by Redescription](https://mlsanity.com/paper/1903.05854/) (2019)
- [Generative Image Modeling using Style and Structure Adversarial Networks](https://mlsanity.com/paper/1603.05631/) (2016)
- [Celer: a Fast Solver for the Lasso with Dual Extrapolation](https://mlsanity.com/paper/1802.07481/) (2018)
- [Pixel-Level Domain Transfer](https://mlsanity.com/paper/1603.07442/) (2016)
- [Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification](https://mlsanity.com/paper/1908.06278/) (2019)
- [XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data](https://mlsanity.com/paper/2105.12807/) (2021)
- [OmiEmbed: a unified multi-task deep learning framework for multi-omics data](https://mlsanity.com/paper/2102.02669/) (2021)
- [Dual Extrapolation for Sparse Generalized Linear Models](https://mlsanity.com/paper/1907.05830/) (2019)

## Abstract (arXiv)

With the rapid development of high-throughput experimental technologies, different types of omics (e.g., genomics, epigenomics, transcriptomics, proteomics, and metabolomics) data can be produced from clinical samples. The correlations between different omics types attracts a lot of research interest, whereas the stduy on genome-wide omcis data translation (i.e, generation and prediction of one type of omics data from another type of omics data) is almost blank. Generative adversarial networks and the variants are one of the most state-of-the-art deep learning technologies, which have shown great success in image-to-image translation, text-to-image translation, etc. Here we proposed OmiTrans, a deep learning framework adopted the idea of generative adversarial networks to achieve omics-to-omics translation with promising results. OmiTrans was able to faithfully reconstruct gene expression profiles from DNA methylation data with high accuracy and great model generalisation, as demonstrated in the experiments.