MOMA: A Multi-task Attention Learning Algorithm for Multi-omics Data Interpretation and Classification
MOMA is a multi-task attention learning model that provides a general classification framework for multi-data.
MOMA can capture important biological processes for high diagnostic performance and interpretability.
The model vectorizes features and modules using a geometric approach, and focuses on important modules in multi-omics data via an attention mechanism.
