# MODIS: Multi-Omics Data Integration for Small and unpaired datasets

arXiv: https://arxiv.org/abs/2503.18856 · HTML: https://mlsanity.com/paper/2503.18856/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [The relativistic discriminator: a key element missing from standard GAN](https://mlsanity.com/paper/1807.00734/) (2018)
- [Geometric GAN](https://mlsanity.com/paper/1705.02894/) (2017)
- [Linearly Mapping from Image to Text Space](https://mlsanity.com/paper/2209.15162/) (2022)
- [Trusted Multi-View Classification](https://mlsanity.com/paper/2102.02051/) (2021)
- [The GAN is dead; long live the GAN! A Modern GAN Baseline](https://mlsanity.com/paper/2501.05441/) (2025)
- [Few-Shot Learning with Class Imbalance](https://mlsanity.com/paper/2101.02523/) (2021)
- [Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning](https://mlsanity.com/paper/2303.11866/) (2023)
- [Multi-modal Deep Learning](https://mlsanity.com/paper/2403.03385/) (2024)
- [Deep Divergence-Based Approach to Clustering](https://mlsanity.com/paper/1902.04981/) (2019)
- [Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment](https://mlsanity.com/paper/2409.19425/) (2024)

## Built on by (most cited first, 1 of 1)

- [ARO: Aligned Representation learning for multi-Omics data](https://mlsanity.com/paper/2610.06443/) (2026)

## Abstract (arXiv)

An important objective in computational biology is the efficient integration of multi-omics data. The task of integration comes with challenges: multi-omics data are most often unpaired (requiring diagonal integration), partially labeled with information about biological conditions, and in some situations such as rare diseases, only very small datasets are available. We present MODIS, a semi supervised framework designed to account for these particular challenges. To address the challenge of very small datasets, we propose to exploit the information contained in larger multi-omics databases by training our model on a large reference database and a small target dataset simultaneously, effectively turning the challenge into a problem of learning with class imbalance. MODIS performs diagonal integration on unpaired samples, leveraging class-labels to align modalities despite class imbalance and data scarcity. The architecture combines multiple variational auto-encoders, a class classifier and an adversarially trained modality classifier. To ensure training stability, we adapted a regularized relativistic GAN loss to this setting. We first validate MODIS on a synthetic dataset to assess the level of supervision needed for accurate alignment and to quantify the impact of class imbalance on predictive performance. We then apply our approach to the large public TCGA database, considering between 10 and 34 classes (cancer types and normal tissue). MODIS demonstrates high prediction accuracy, robust performance with limited supervision, and stability to class imbalance. These results position MODIS as a promising solution for challenging integration scenarios, particularly diagonal integration with a small number of samples, typical of rare diseases studies. The code is available at https://github.com/VILLOUTREIXLab/MODIS.