  MicroRNAs (miRNAs) play crucial roles in multifarious biological processes
associated with human diseases. Identifying potential miRNA-disease
associations contributes to understanding the molecular mechanisms of
miRNA-related diseases. Most of the existing computational methods mainly focus
on predicting whether a miRNA-disease association exists or not. However, the
roles of miRNAs in diseases are prominently diverged, for instance, Genetic
variants of microRNA (mir-15) may affect expression level of miRNAs leading to
B cell chronic lymphocytic leukemia, while circulating miRNAs (including
mir-1246, mir-1307-3p, etc.) have potentials to detecting breast cancer in the
early stage. In this paper, we aim to predict multi-type 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.
