Combined Genomics Modality

Combined genomics modality research focuses on integrating diverse genomic datasets (e.g., RNA expression, methylation, microRNA) to improve the accuracy and robustness of downstream predictive tasks, such as disease prognosis or classification. Current research employs deep learning architectures, including variational autoencoders and multitask learning models, often incorporating techniques like normalizing flows and imputation methods to handle missing data and enhance model generalizability. These approaches aim to leverage the synergistic information across different genomic modalities, ultimately leading to more powerful and reliable predictive models in biomedical applications. The improved predictive power from integrating these data sources has significant implications for personalized medicine and clinical decision-making.