Inferring human miRNA-disease associations via multiple kernel fusion on GCNII

Shanghui Lu; Yong Liang; Le Li; Shuilin Liao; Dong Ouyang

doi:10.3389/fgene.2022.980497

Inferring human miRNA-disease associations via multiple kernel fusion on GCNII

Front Genet. 2022 Sep 5:13:980497. doi: 10.3389/fgene.2022.980497. eCollection 2022.

Authors

Shanghui Lu^{1

2}, Yong Liang^{1

3}, Le Li¹, Shuilin Liao¹, Dong Ouyang¹

Affiliations

¹ School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, China.
² School of Mathematics and Physics, Hechi University, Hechi, China.
³ Peng Cheng Laboratory, Shenzhen, China.

Abstract

Increasing evidence shows that the occurrence of human complex diseases is closely related to the mutation and abnormal expression of microRNAs(miRNAs). MiRNAs have complex and fine regulatory mechanisms, which makes it a promising target for drug discovery and disease diagnosis. Therefore, predicting the potential miRNA-disease associations has practical significance. In this paper, we proposed an miRNA-disease association predicting method based on multiple kernel fusion on Graph Convolutional Network via Initial residual and Identity mapping (GCNII), called MKFGCNII. Firstly, we built a heterogeneous network of miRNAs and diseases to extract multi-layer features via GCNII. Secondly, multiple kernel fusion method was applied to weight fusion of embeddings at each layer. Finally, Dual Laplacian Regularized Least Squares was used to predict new miRNA-disease associations by the combined kernel in miRNA and disease spaces. Compared with the other methods, MKFGCNII obtained the highest AUC value of 0.9631. Code is available at https://github.com/cuntjx/bioInfo.

Keywords: GCNII; deep GCN; dual laplacian regularized least squares; miRNA-disease associations; multiple kernel fusion.