ITRPCA: a new model for computational drug repositioning based on improved tensor robust principal component analysis.

ABSTRACT: Background: 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. Methods: 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 k-nearest neighbor (WKNN) approach to increase the overall density of

SUBMITTER: Yang M 

PROVIDER: S-EPMC10545866 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature