MOSA - Multi-Omic Synthetic Augmentation
High-quality multi-omic data is scarce and expensive to generate. To overcome this limitation, we developed MOSA (Multi-Omic Synthetic Augmentation), a generative AI model based on variational autoencoders. MOSA learns the underlying distribution of multi-omic data and generates realistic synthetic profiles that can be used to augment existing datasets. This approach increases the statistical power of downstream analyses, such as biomarker discovery and drug response prediction, and helps to address the issue of missing data in multi-omic integration. Our work, published in *Nature Communications*, demonstrates the potential of generative AI to enhance cancer data science.
High-quality multi-omic data is scarce and expensive to generate. To overcome this limitation, we developed MOSA (Multi-Omic Synthetic Augmentation), a generative AI model based on variational autoencoders. MOSA learns the underlying distribution of multi-omic data and generates realistic synthetic profiles that can be used to augment existing datasets. This approach increases the statistical power of downstream analyses, such as biomarker discovery and drug response prediction, and helps to address the issue of missing data in multi-omic integration. Our work, published in Nature Communications, demonstrates the potential of generative AI to enhance cancer data science.