Tensor Decomposition with Relational Constraints for Predicting Multiple Types of MicroRNA-disease Associations

TL;DR

This paper introduces a tensor decomposition approach with relational constraints to predict multiple types of miRNA-disease associations, improving accuracy over existing methods by incorporating biological features.

Contribution

The paper proposes a novel tensor decomposition method with relational constraints (TDRC) for multi-type miRNA-disease association prediction, enhancing performance and efficiency.

Findings

TDRC outperforms baseline methods with up to 38% improvement in top-1 F1 score.

Tensor representation effectively captures multi-type associations.

Incorporating biological features as relational constraints improves prediction accuracy.

Abstract

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.