Accurately identifying new therapeutic uses for drugs is crucial for advancing pharmaceutical research and development. Matrix factorization is often used in association prediction due to its simplicity and high interpretability. However, existing matrix factorization models do not enable real-time interaction between molecular feature matrices and similarity matrices, nor do they consider the geometric structure of the matrices. Additionally, efficiently integrating multisource data remains a significant challenge. To address these issues, we propose a two-tier interactive weighted matrix factorization and label propagation model based on similarity matrix fusion (TIWMFLP) to assist in personalized treatment. First, we calculate the Gaussian and Laplace kernel similarities for drugs and diseases using known drug-disease associations. We then introduce a new multisource similarity fusion method, called similarity matrix fusion (SMF), to integrate these drug/disease similarities. SMF not only considers the different contributions represented by each neighbor but also incorporates drug-disease association information to enhance the contextual topological relationships and potential features of each drug/disease node in the network. Second, we innovatively developed a two-tier interactive weighted matrix factorization (TIWMF) method to process three biological networks. This method realizes for the first time the real-time interaction between the drug/disease feature matrix and its similarity matrix, allowing for a better capture of the complex relationships between drugs and diseases. Additionally, the weighted matrix of the drug/disease similarity matrix is introduced to preserve the underlying structure of the similarity matrix. Finally, the label propagation algorithm makes predictions based on the three updated biological networks. Experimental outcomes reveal that TIWMFLP consistently surpasses state-of-the-art models on four drug-disease data sets, two small molecule-miRNA data sets, and one miRNA-disease data set.

中文翻译：

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TIWMFLP： 基于相似性矩阵融合的两层交互式加权矩阵分解和标签传播，用于药物疾病关联预测


准确识别药物的新治疗用途对于推进药物研发至关重要。矩阵分解由于其简单性和高可解释性而经常用于关联预测。然而，现有的矩阵分解模型不支持分子特征矩阵和相似矩阵之间的实时交互，也不考虑矩阵的几何结构。此外，有效集成多源数据仍然是一项重大挑战。为了解决这些问题，我们提出了一种基于相似性矩阵融合 （TIWMFLP） 的两层交互式加权矩阵分解和标签传播模型，以协助个性化治疗。首先，我们使用已知的药物-疾病关联计算药物和疾病的高斯和拉普拉斯核相似性。然后，我们引入了一种新的多源相似性融合方法，称为相似性矩阵融合 （SMF），以整合这些药物/疾病相似性。SMF 不仅考虑了每个邻居所代表的不同贡献，还结合了药物-疾病关联信息，以增强网络中每个药物/疾病节点的上下文拓扑关系和潜在特征。其次，我们创新性地开发了一种两层交互式加权矩阵分解 （TIWMF） 方法来处理三个生物网络。该方法首次实现了药物/疾病特征矩阵与其相似性矩阵之间的实时交互，从而可以更好地捕获药物与疾病之间的复杂关系。此外，还引入了药物/疾病相似性矩阵的加权矩阵，以保留相似性矩阵的底层结构。 最后，标签传播算法根据三个更新的生物网络进行预测。实验结果表明，TIWMFLP 在 4 个药物疾病数据集、2 个小分子 miRNA 数据集和 1 个 miRNA 疾病数据集上始终优于最先进的模型。