Abstract:

Most studies in drug repositioning field rely on the “similar drugs treat similar diseases” hypothesis， requiring similarity data between diseases and drugs. However， such data has acquisition difficulties， different methods have significant discrepancies in computational results， and the research has inability to conduct when data is missing. To address these issues， this study proposed a drug repositioning method， EMP-PCA， based on Empirical Meta-Path （EMP） and Principal Component Analysis （PCA） that achieved drug-disease association prediction without similarity data. First， five meta-paths corresponding to different interaction data were introduced to generate a commuting matrix for mining multi-source correlation information. Second， PCA was employed to identify the directions with the highest variances， dimensionality reduction was performed through data projection， and computations were simplified while retaining core information. Finally， Gradient Boosting Tree （GBT） method was used to construct base classifiers for various meta-paths， and the classifiers were combined into an ensemble classifier to integrate multi-source data effectively. Experimental results of comparing EMP-PCA with similarity data-based methods like Drug Repurposing via Heterogeneous Graph Convolutional Network （DRHGCN）， Additional Neural Matrix Factorization （ANMF） model， and Layer-wise Attention Graph Convolutional Network （LAGCN） demonstrate that EMP-PCA can fuse multi-source interaction data among drugs， proteins， and diseases without requiring similarity data effectively， and the method outperforms the comparison methods in key evaluation metrics including Area Under Curve （AUC）， precision， and recall， resolving data dependency and missing value issues inherent in similarity-based methods effectively， and has high practical application value.

Key words: drug repositioning, Empirical Meta-Path (EMP), commuting matrix, Principal Component Analysis (PCA), Gradient Boosting Tree (GBT), interaction data, ensemble classifier

摘要：

药物重定位领域多数研究依赖“相似药物治疗相似疾病”假设，因此需使用疾病和药物等相似性数据，而此类数据存在获取困难、不同计算方法结果差异大且数据缺失时研究无法开展的难题，提出一种基于元路径（EMP）与主成分分析（PCA）的药物重定位方法（EMP-PCA），从而无需相似性数据即可实现药物-疾病关联预测。首先，引入对应不同相互作用数据的5条元路径，以生成交换矩阵来挖掘多源关联信息；其次，通过PCA找寻方差最大的方向，对数据进行投影降维，并在简化计算的同时保留核心信息；最后，利用梯度提升树方法为每条元路径构建基分类器，并把它们组合成集成分类器，从而实现多源数据的有效整合。将EMP-PCA与药物重定位异质图卷积网络（DRHGCN）、附加神经矩阵分解（ANMF）模型、层注意力图卷积网络（LAGCN）等基于相似性数据的药物重定位方法进行对比。实验结果表明，EMP-PCA无需引入任何相似性数据，即可有效融合药物、蛋白质与疾病间的多源相互作用数据，且在曲线下面积（AUC）、精确率和召回率等关键评价指标上均优于对比方法，能有效解决基于相似性方法的数据依赖与缺失难题，具备优异的关联预测性能和较高的实际应用价值。

关键词: 药物重定位, 元路径, 交换矩阵, 主成分分析, 梯度提升树, 相互作用数据, 集成分类器