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Abstract <p>Computational drug repurposing utilizes data analysis and predictive models to identify new uses for existing drugs and new drugs, significantly improving research efficiency and reducing costs compared to traditional screening methods. Due to the limitations of current computational models in extracting deep key features, we develop a novel drug repurposing model based on the deep non-negative matrix factorization (DNMF-DDA) to enhance the accuracy of drug-disease association predictions. The model leverages similarity and known association data to extract low-rank features from complex data spaces, allowing for the prediction of potential drug-disease associations. To improve performance for novel drugs, we apply the <i>k</i>-nearest neighbors (KNN) algorithm for preprocessing, increasing the density of the matrix’s prior information. Next, we construct two integrated matrices based on the similarities of drugs and diseases, respectively, and the optimized association data. During deep matrix factorization, we incorporate graph Laplacian and relaxed regularization constraints to optimize local graph features. This multi-layer optimization enhances the model’s understanding of complex drug-disease relationships, effectively mitigating the negative impact of insufficient prior information during cold-start tests. Furthermore, we incorporate non-negativity constraints to ensure that the prediction results are biologically meaningful. To evaluate the performance of DNMF-DDA, we conducted cold-start test and 10-fold cross-validation on three datasets and systematically compared it with five state-of-the-art drug repurposing methods. The results demonstrate that DNMF-DDA performs exceptionally well in predicting drug-disease associations, significantly outperforming existing approaches. Our proposed method not only efficiently handles high-dimensional data but also exhibits superior performance, providing new insights for drug development. Moreover, the case study further validated the significant practical value of the DNMF-DDA model in practical applications.</p> Graphical Abstract <p></p>

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A Novel Drug–Disease Association Prediction Method Based on Deep Non-Negative Matrix Factorization with Local Graph Feature

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月7日

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  • Mengyun Yang,
  • Bin Yang,
  • Jiajun Chen,
  • Xiwei Tang,
  • Guihua Duan

摘要

Abstract

Computational drug repurposing utilizes data analysis and predictive models to identify new uses for existing drugs and new drugs, significantly improving research efficiency and reducing costs compared to traditional screening methods. Due to the limitations of current computational models in extracting deep key features, we develop a novel drug repurposing model based on the deep non-negative matrix factorization (DNMF-DDA) to enhance the accuracy of drug-disease association predictions. The model leverages similarity and known association data to extract low-rank features from complex data spaces, allowing for the prediction of potential drug-disease associations. To improve performance for novel drugs, we apply the k-nearest neighbors (KNN) algorithm for preprocessing, increasing the density of the matrix’s prior information. Next, we construct two integrated matrices based on the similarities of drugs and diseases, respectively, and the optimized association data. During deep matrix factorization, we incorporate graph Laplacian and relaxed regularization constraints to optimize local graph features. This multi-layer optimization enhances the model’s understanding of complex drug-disease relationships, effectively mitigating the negative impact of insufficient prior information during cold-start tests. Furthermore, we incorporate non-negativity constraints to ensure that the prediction results are biologically meaningful. To evaluate the performance of DNMF-DDA, we conducted cold-start test and 10-fold cross-validation on three datasets and systematically compared it with five state-of-the-art drug repurposing methods. The results demonstrate that DNMF-DDA performs exceptionally well in predicting drug-disease associations, significantly outperforming existing approaches. Our proposed method not only efficiently handles high-dimensional data but also exhibits superior performance, providing new insights for drug development. Moreover, the case study further validated the significant practical value of the DNMF-DDA model in practical applications.

Graphical Abstract