Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction.

ABSTRACT: Identifying new therapeutic indications for existing drugs is a major challenge in drug repositioning. Most computational drug repositioning methods focus on known targets. Analyzing multiple aspects of various protein associations provides an opportunity to discover underlying drug-associated proteins that can be used to improve the performance of the drug repositioning approaches. In this study, machine learning models were developed based on the similarities of diversified biological features, including protein interaction, topological network, sequence alignment, and biological function to predict protein pairs associating with the same drugs. The crucial set of features was identified, and the high performances of protein pair predictions were achieved with an area under the curve (AU

SUBMITTER: Kitsiranuwat S 

PROVIDER: S-EPMC10358641 | biostudies-literature | 2022 Jul-Sep

REPOSITORIES: biostudies-literature