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DoGMA is a central-dogma-guided foundation model for pan-cancer multi-omics analysis, using a Transformer-MoE architecture with directed attention to align DNA-RNA-protein flows and pretraining via masked hierarchical omics reconstruction. It shows strong performance across cancer representation learning, survival prediction, and metastasis prediction tasks.
This paper systematically benchmarks classical machine learning models (Random Forest, XGBoost, etc.) for ER status prediction using multi-omics data from TCGA-BRCA, finding that RNA expression provides the strongest predictive signal and that Random Forest achieves 90.3% balanced accuracy in the integrated multi-omic setting.
MIT released FINGERS-7B, a 7-billion-parameter multi-omics foundation model trained on data from 30,000 individuals to predict Alzheimer's risk years in advance. The model is accessible via the AD Workbench and is accompanied by a research paper on OpenReview.