Heterogeneous graph inference with range constrainted L2,1-collaborative matrix factorization for small molecule-miRNA association prediction

Shudong Wang; Tiyao Liu; Chuanru Ren; Yawu Zhao; Sibo Qiao; Yuanyuan Zhang; Shanchen Pang

doi:10.1016/j.compbiolchem.2024.108078

Heterogeneous graph inference with range constrainted L_2,1-collaborative matrix factorization for small molecule-miRNA association prediction

Comput Biol Chem. 2024 Apr 20:110:108078. doi: 10.1016/j.compbiolchem.2024.108078. Online ahead of print.

Authors

Shudong Wang¹, Tiyao Liu¹, Chuanru Ren¹, Yawu Zhao¹, Sibo Qiao¹, Yuanyuan Zhang², Shanchen Pang¹

Affiliations

¹ College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China.
² School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China. Electronic address: zhangyuanyuan@qut.edu.cn.

PMID: 38677013
DOI: 10.1016/j.compbiolchem.2024.108078

Abstract

MicroRNAs (miRNAs) play a vital role in regulating gene expression and various biological processes. As a result, they have been identified as effective targets for small molecule (SM) drugs in disease treatment. Heterogeneous graph inference stands as a classical approach for predicting SM-miRNA associations, showcasing commendable convergence accuracy and speed. However, most existing methods do not adequately address the inherent sparsity in SM-miRNA association networks, and imprecise SM/miRNA similarity metrics reduce the accuracy of predicting SM-miRNA associations. In this research, we proposed a heterogeneous graph inference with range constrained L_2,1-collaborative matrix factorization (HGIRCLMF) method to predict potential SM-miRNA associations. First, we computed the multi-source similarities of SM/miRNA and integrated these similarity information into a comprehensive SM/miRNA similarity. This step improved the accuracy of SM and miRNA similarity, ensuring reliability for the subsequent inference of the heterogeneity map. Second, we used a range constrained L_2,1-collaborative matrix factorization (RCLMF) model to pre-populate the SM-miRNA association matrix with missing values. In this step, we developed a novel matrix decomposition method that enhances the robustness and formative nature of SM-miRNA edges between SM networks and miRNA networks. Next, we built a well-established SM-miRNA heterogeneous network utilizing the processed biological information. Finally, HGIRCLMF used this network data to infer unknown association pair scores. We implemented four cross-validation experiments on two distinct datasets, and HGIRCLMF acquired the highest areas under the curve, surpassing six state-of-the-art computational approaches. Furthermore, we performed three case studies to validate the predictive power of our method in practical application.

Keywords: Constrained matrix factorization; Heterogeneous graph inference; L(2,1)-norm regularization; Multi-source similarity; SM-miRNA association prediction.