Self-Normalizing Foundation Model for Enhanced Multi-Omics Data Analysis in Oncology

Understand

Multi-omics research has enhanced our understanding of cancer heterogeneity and progression.

• Investigating molecular data through multi-omics approaches is crucial for unraveling the complex biological mechanisms underlying cancer, thereby enabling more effective diagnosis, treatment, and prevention strategies.
• However, predicting patient outcomes through the integration of all available multi-omics data is still an under-study research direction.
• Here, we present SeNMo, a foundation model that has been trained on multi-omics data across 33 cancer types.