Before revision:  Predicting miRNA-disease associations and types is critical to understand the underlying pathogenesis of human diseases from the molecular level . Results: In this paper, we formulate the problem as a link prediction in knowledge graphs. We use biomedical knowledge bases to build a knowledge graph of entities representing miRNAs and disease and multi-relations, and we propose a tensor decomposition-based model named TDRC to predict  miRNA-disease associations and their types from the knowledge graph.
After revision:  Predicting miRNA-disease associations and types is critical to understand the underlying pathogenesis of human diseases from the molecular level . Results: In this paper, we aim to predict miRNA-disease associations and their types from the knowledge graph.