Before revision:  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. We have experimentally evaluated our methodand compared it to several baseline methods. The results demonstrate that the proposed method has high-accuracy and high-efficiency performances .
After revision:  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 instead of taking them as binary. To this end, we innovatively represent miRNA-disease-type triplets as a tensor and introduce Tensor Decomposition methods to solve the prediction task. Experimental results on two widely-adopted miRNA-disease datasets: HMDD v2.0 and HMDD v3.2 show that tensor decomposition methods improve a recent baseline in a large scale (up to 38\% in top-1 F1). We further propose a novel method, Tensor Decomposition with Relational Constraints (TDRC), which incorporates biological features as relational constraints to further the existing tensor decomposition methods. Compared with two existing tensor decomposition methods, TDRC can produce better performance while being more efficient .