HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization.

Abstract

Existing matrix factorization methods face challenges, including the cold start problem and global nonlinear data loss during similarity learning, particularly in predicting associations between long noncoding RNAs (LncRNAs) and diseases. To overcome these issues, we introduce HPTRMF, a matrix factorization approach incorporating high-order perturbation and flexible trifactor regularization. HPTRMF constructs a high-order correlation matrix utilizing the known association matrix, leveraging high-order perturbation to effectively address the cold start problem caused by data sparsity. Additionally, HPTRMF incorporates a flexible trifactor regularization term to capture similarity information on LncRNAs and diseases, enabling the effective handling of global nonlinear data loss by capturing such data in the similarity matrix. Experimental results demonstrate the superiority of HPTRMF over nine state-of-the-art algorithms in Leave-One-Out Cross-Validation (LOOCV) and Five-Fold Cross-Validation (5-Fold CV) on three data sets.HPTRMF and data sets are available in https://github.com/Llvvvv/HPTRMF.