MLOmics: Benchmark for Machine Learning on Cancer Multi-Omics Data

2024-09-02

AI Summary

Existing public cancer multi-omics databases (e.g., TCGA, LinkedOmics) lack machine learning–oriented standardized preprocessing and unified programmatic interfaces, hindering model development and reproducibility. To address this, we propose MLOmics—the first plug-and-play multi-omics machine learning benchmark for cancer. It integrates data from 8,314 patients across 32 cancer types sourced primarily from TCGA, spanning genomic, transcriptomic, methylomic, and proteomic modalities. MLOmics provides standardized data curation, cross-platform feature alignment, stratified sampling, and integration with the BioLink knowledge graph. The open-source benchmark includes a complete end-to-end data pipeline, performance reports for 12 baseline models, and illustrative cross-omics fusion analysis cases. By unifying data access, preprocessing, and evaluation protocols, MLOmics significantly improves model reproducibility, cross-cancer generalizability, and biological interpretability—thereby bridging the critical gap between multi-omics resources and ML-driven cancer research.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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 propose MLOmics, an open cancer multi-omics benchmark 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.

Problem

• Lack of ready-to-use multi-omics datasets for machine learning models in cancer research.
• Need for high-quality, preprocessed datasets to improve cancer multi-omics analysis.
• Development of MLOmics benchmark to support bioinformatics and machine learning model evaluation.

Innovation

• MLOmics benchmark for cancer multi-omics data
• Includes 8,314 patient samples across 32 cancers
• Supports bioinformatics and machine learning model development