Yang Mengyun, Yang Bin, Duan Guihua, Wang Jianxin
School of Mechanical and Energy Engineering, Shaoyang University, Shaoyang, China.
School of Computer Science, Hunan First Normal University, Changsha, China.
Front Genet. 2023 Sep 18;14:1271311. doi: 10.3389/fgene.2023.1271311. eCollection 2023.
Drug repositioning is considered a promising drug development strategy with the goal of discovering new uses for existing drugs. Compared with the experimental screening for drug discovery, computational drug repositioning offers lower cost and higher efficiency and, hence, has become a hot issue in bioinformatics. However, there are sparse samples, multi-source information, and even some noises, which makes it difficult to accurately identify potential drug-associated indications. In this article, we propose a new scheme with improved tensor robust principal component analysis (ITRPCA) in multi-source data to predict promising drug-disease associations. First, we use a weighted -nearest neighbor (WKNN) approach to increase the overall density of the drug-disease association matrix that will assist in prediction. Second, a drug tensor with five frontal slices and a disease tensor with two frontal slices are constructed using multi-similarity matrices and an updated association matrix. The two target tensors naturally integrate multiple sources of data from the drug-side aspect and the disease-side aspect, respectively. Third, ITRPCA is employed to isolate the low-rank tensor and noise information in the tensor. In this step, an additional range constraint is incorporated to ensure that all the predicted entry values of a low-rank tensor are within the specific interval. Finally, we focus on identifying promising drug indications by analyzing drug-disease association pairs derived from the low-rank drug and low-rank disease tensors. We evaluate the effectiveness of the ITRPCA method by comparing it with five prominent existing drug repositioning methods. This evaluation is carried out using 10-fold cross-validation and independent testing experiments. Our numerical results show that ITRPCA not only yields higher prediction accuracy but also exhibits remarkable computational efficiency. Furthermore, case studies demonstrate the practical effectiveness of our method.
药物重新定位被认为是一种很有前景的药物开发策略，其目标是发现现有药物的新用途。与用于药物发现的实验性筛选相比，计算药物重新定位成本更低、效率更高，因此已成为生物信息学中的一个热点问题。然而，存在样本稀疏、多源信息甚至一些噪声，这使得难以准确识别潜在的药物相关适应症。在本文中，我们提出了一种新方案，即改进的多源数据张量鲁棒主成分分析（ITRPCA），以预测有前景的药物-疾病关联。首先，我们使用加权k近邻（WKNN）方法来提高药物-疾病关联矩阵的整体密度，这将有助于预测。其次，使用多相似性矩阵和更新后的关联矩阵构建一个具有五个前切片的药物张量和一个具有两个前切片的疾病张量。这两个目标张量分别自然地整合了来自药物方面和疾病方面的多源数据。第三，采用ITRPCA来分离张量中的低秩张量和噪声信息。在这一步中，引入了一个额外的范围约束，以确保低秩张量的所有预测条目值都在特定区间内。最后，我们通过分析从低秩药物张量和低秩疾病张量导出的药物-疾病关联对来专注于识别有前景的药物适应症。我们通过将ITRPCA方法与五种现有的著名药物重新定位方法进行比较来评估其有效性。这种评估是使用10折交叉验证和独立测试实验进行的。我们的数值结果表明，ITRPCA不仅产生更高的预测准确率，而且表现出显著的计算效率。此外，案例研究证明了我们方法的实际有效性。
