TY  - JOUR
AU  - Zhu, Lingzhi 
AU  - Gao, Weimin 
AU  - Li, Guixiang 
AU  - Ge, Bufan 
AU  - Zhang, Bohan 
AU  - Cao, Yulu 
PY  - 2021
TI  - KRLSMDA: Identifying Human miRNA–Disease Association Based on Similarity and Kronecker Regularized Least Squares Method
JF  - American Journal of Biochemistry and Biotechnology
VL  - 17
IS  - 1
DO  - 10.3844/ajbbsp.2021.76.84
UR  - https://thescipub.com/abstract/ajbbsp.2021.76.84
AB  - A growing number of studies have suggested that miRNAs (microRNAs) have associations with human diseases, the design and discovery of drug. But so far, we do not yet fully understand the molecular mechanism of miRNAs in the development of human diseases. Predicting miRNA-disease associations is helpful for understanding the molecular mechanism of miRNAs in the development of human diseases. However, wet-lab experiments are time-consuming and need higher costs to discover miRNA-disease associations. Some computational methods are proposed for predicting miRNA-disease associations, but the prediction performance of these methods needs to be further improved. In this study, we propose a new computational model (KRLSMDA) based on similarity and the Kronecker Regularized Least Squares algorithm. In KRLSMDA, the miRNA functional similarity, the miRNA sequence similarity and the Gaussian Interaction Profile (GIP) kernel similarity are integrated into the comprehensive miRNA similarity. Then we compute the disease semantic similarity, disease functional similarity and the GIP kernel similarity to construct the comprehensive disease similarity based on the disease semantic information, the disease functional information and known miRNA-disease associations, respectively. Finally, the kronecker regularized least squares algorithm is used to predict hidden miRNA-disease associations. The experimental results show that KRLSMDA has achieved the average Area Under the Curve (AUC) values of 0.9181±0.032 and 0.9267±0.022 in 5-fold Cross-Validation (5CV) and 10-fold Cross-Validation (10CV), respectively, which demonstrates KRLSMDA is superior to four competing models. We expect KRLSMDA to be a supplement in the field of biomedical research in the future.