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• W3015692714 endingPage "2776" @default.
• W3015692714 startingPage "2776" @default.
• W3015692714 abstract "A gene expression signature (GES) is a group of genes that shows a unique expression profile as a result of perturbations by drugs, genetic modification or diseases on the transcriptional machinery. The comparisons between GES profiles have been used to investigate the relationships between drugs, their targets and diseases with quite a few successful cases reported. Especially in the study of GES-guided drugs-disease associations, researchers believe that if a GES induced by a drug is opposite to a GES induced by a disease, the drug may have potential as a treatment of that disease. In this study, we data-mined the crowd extracted expression of differential signatures (CREEDS) database to evaluate the similarity between GES profiles from drugs and their indicated diseases. Our study aims to explore the application domains of GES-guided drug-disease associations through the analysis of the similarity of GES profiles on known pairs of drug-disease associations, thereby identifying subgroups of drugs/diseases that are suitable for GES-guided drug repositioning approaches. Our results supported our hypothesis that the GES-guided drug-disease association method is better suited for some subgroups or pathways such as drugs and diseases associated with the immune system, diseases of the nervous system, non-chemotherapy drugs or the mTOR signaling pathway." @default.
• W3015692714 created "2020-04-17" @default.
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• W3015692714 date "2020-06-16" @default.
• W3015692714 modified "2023-10-18" @default.
• W3015692714 title "The Performance of Gene Expression Signature-Guided Drug–Disease Association in Different Categories of Drugs and Diseases" @default.
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• W3015692714 doi "https://doi.org/10.3390/molecules25122776" @default.
• W3015692714 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7357095" @default.
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• W3015692714 hasPublicationYear "2020" @default.
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