Benchmarking Multimodal Biological Data Integration and Imputation

Biological data serves as the foundation for analyzing life systems. However, heterogeneous feature spaces and technical noise severely hinder the integration and imputation of biological data. The high cost of acquiring joint measurements across modalities further limits analysis capabilities. There is an urgent need for deep learning methods to efficiently integrate and impute unpaired multimodal biological data, enabling a more comprehensive understanding of cellular behavior. We collected widely used multimodal biological data integration and imputation methods and established a comprehensive benchmark to advance this field.