# OmiEmbed: a unified multi-task deep learning framework for multi-omics data

arXiv: https://arxiv.org/abs/2102.02669 · HTML: https://mlsanity.com/paper/2102.02669/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 57 references that are in the map.
- First posted on arXiv 3 Feb 2021.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification](https://mlsanity.com/paper/1711.05859/) (2017)
- [Intra-Processing Methods for Debiasing Neural Networks](https://mlsanity.com/paper/2006.08564/) (2020)
- [Unbiased split variable selection for random survival forests using maximally selected rank statistics](https://mlsanity.com/paper/1605.03391/) (2016)
- [Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification](https://mlsanity.com/paper/1908.06278/) (2019)
- [AffinityNet: semi-supervised few-shot learning for disease type prediction](https://mlsanity.com/paper/1805.08905/) (2018)
- [Cross-Domain Medical Image Translation by Shared Latent Gaussian Mixture Model](https://mlsanity.com/paper/2007.07230/) (2020)

## Built on by (most cited first, 12 of 12)

- [XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data](https://mlsanity.com/paper/2105.12807/) (2021)
- [CustOmics: A versatile deep-learning based strategy for multi-omics integration](https://mlsanity.com/paper/2209.05485/) (2022)
- [SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification](https://mlsanity.com/paper/2202.01672/) (2022)
- [Coupling Deep Imputation with Multitask Learning for Downstream Tasks on Genomics Data](https://mlsanity.com/paper/2204.13705/) (2022)
- [Self-omics: A Self-supervised Learning Framework for Multi-omics Cancer Data](https://mlsanity.com/paper/2210.00825/) (2022)
- [URLOST: Unsupervised Representation Learning without Stationarity or Topology](https://mlsanity.com/paper/2310.04496/) (2023)
- [OmiTrans: generative adversarial networks based omics-to-omics translation framework](https://mlsanity.com/paper/2111.13785/) (2021)
- [A Fair Experimental Comparison of Neural Network Architectures for Latent Representations of Multi-Omics for Drug Response Prediction](https://mlsanity.com/paper/2208.14822/) (2022)
- [DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data](https://mlsanity.com/paper/2307.04075/) (2023)
- [Self-supervised learning of multi-omics embeddings in the low-label, high-data regime](https://mlsanity.com/paper/2311.09962/) (2023)
- [Artificial Intelligence and Deep Learning Algorithms for Epigenetic Sequence Analysis: A Review for Epigeneticists and AI Experts](https://mlsanity.com/paper/2504.03733/) (2025)
- [Latent World Recovery for Multimodal Learning with Missing Modalities](https://mlsanity.com/paper/2606.12362/) (2026)

## Abstract (arXiv)

High-dimensional omics data contains intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data due to the large number of molecular features and small number of available samples, which is also called 'the curse of dimensionality' in machine learning. To tackle this problem and pave the way for machine learning aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed support multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy comparing to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various application of high-dimensional omics data and has a great potential to facilitate more accurate and personalised clinical decision making.