ITRPCA: a new model for computational drug repositioning based on improved tensor robust principal component analysis - Yang, Mengyun; Yang, Bin; Duan, Guihua; Wang, Jianxin (Frontiers in Genetics, 2023)
Background: Drug repositioning is considered to be 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 tends to be lower cost and higher efficiency, which has become a hot issue in bioinformatics. However, there are sparse samples, multi-source information, and even some noises, which makes it difficult to identify potential drug-associated indications accurately.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. Firstly, we use a weighted k-nearest neighbor (WKNN) approach to increase overall density of drug-disease association matrix that will assist in prediction. Secondly, a drug tensor with five frontal slices and a disease tensor with two frontal slices are constructed by using multi-similarity matrices and updated association matrix. The two target tensors naturally integrate multiple sources of data from the drug-side aspect and the disease-side aspect, respectively.Thirdly, the 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 predicted entry values of 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 tensor and low-rank disease tensor.We evaluate the effectiveness of the ITRPCA method by comparing it with five prominent 1 Yang et al.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.