# MLOmics: Cancer Multi-Omics Database for Machine Learning

arXiv: https://arxiv.org/abs/2409.02143 · HTML: https://mlsanity.com/paper/2409.02143/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 2 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [Handling Missing Data with Graph Representation Learning](https://mlsanity.com/paper/2010.16418/) (2020)
- [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)
- [wTO: an R package for computing weighted topological overlap and consensus networks with an integrated visualization tool](https://mlsanity.com/paper/1711.04702/) (2017)
- [Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization](https://mlsanity.com/paper/2206.10801/) (2022)

## Built on by (most cited first, 7 of 7)

- [TabPFN-Wide: Continued Pre-Training for Extreme Feature Counts](https://mlsanity.com/paper/2510.06162/) (2025)
- [K-Dense Analyst: Towards Fully Automated Scientific Analysis](https://mlsanity.com/paper/2508.07043/) (2025)
- [CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification](https://mlsanity.com/paper/2604.24201/) (2026)
- [CacheMind: From Miss Rates to Why -- Natural-Language, Trace-Grounded Reasoning for Cache Replacement](https://mlsanity.com/paper/2602.12422/) (2026)
- [CoVAE: correlated multimodal generative modeling](https://mlsanity.com/paper/2603.01965/) (2026)
- [OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data](https://mlsanity.com/paper/2605.15511/) (2026)
- [Pretraining Reusable Inference Across Views with Synthetic Task Priors](https://mlsanity.com/paper/2608.19115/) (2026)

## Abstract (arXiv)

Framing the investigation of diverse cancers as a machine learning problem has recently shown significant potential in multi-omics analysis and cancer research. Empowering these successful machine learning models are the high-quality training datasets with sufficient data volume and adequate preprocessing. However, while there exist several public data portals, including The Cancer Genome Atlas (TCGA) multi-omics initiative or open-bases such as the LinkedOmics, these databases are not off-the-shelf for existing machine learning models. In this paper, we introduce MLOmics, an open cancer multi-omics database aiming at serving better the development and evaluation of bioinformatics and machine learning models. MLOmics contains 8,314 patient samples covering all 32 cancer types with four omics types, stratified features, and extensive baselines. Complementary support for downstream analysis and bio-knowledge linking are also included to support interdisciplinary analysis.