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

doi:10.1021/acs.jcim.4c01070

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Article

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

Journal of chemical information and modeling - 23 Dec 2024

Xie Guobo, Li Dayin, Lin Zhiyi, Gu Guosheng, Li Weijun, Chen Ruibin, Liu Zhenguo

Abstract excerpt

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...

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PMID: 39058598
DOI: 10.1021/acs.jcim.4c01070

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