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• W4387092334 abstract "Amyloid transthyretin (ATTR) amyloidosis caused by transthyretin misfolded into amyloid deposits in nerve and heart is a progressive rare disease. The unknown pathogenesis and the lack of therapy make the 5-year survival prognosis extremely poor. Currently available ATTR drugs can only relieve symptoms and slow down progression, but no drug has demonstrated curable effective for this disease. The growing volume of pharmacological data and large-scale genome and transcriptome data bring new opportunities to find potential new ATTR drugs through computational drug repositioning. We collected the ATTR-related in the disease pathogenesis and differentially expressed (DE) genes from five public databases and GEO expression profiles respectively, then screened drug candidates by a corrected protein-protein network analysis of the ATTR-related genes as well as the drug targets from DrugBank database, and then filtered the drug candidates on the basis of gene expression data perturbed by compounds. We collected 139 and 56 ATTR-related genes from five public databases and transcriptome data respectively, and performed functional enrichment analysis. We screened out 355 drug candidates based on the proximity to ATTR-related genes in the corrected interactome network, refined by Graph Neural Networks (GNN). An Inverted Gene Set Enrichment analysis was further applied to estimate the effect of perturbations on ATTR-related and differential expression (DE) genes. High probability drug candidates were discussed. Drug repositioning using systematic computational processes on an interactome network with transcriptome data were performed to screen out several potential new drug candidates for ATTR." @default.
• W4387092334 created "2023-09-28" @default.
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• W4387092334 date "2023-09-27" @default.
• W4387092334 modified "2023-09-29" @default.
• W4387092334 title "Drug repositioning for ATTR amyloidosis by interactome network corrected by Graph Neural Networks and transcriptome analysis" @default.
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• W4387092334 doi "https://doi.org/10.1089/hum.2021.222" @default.
• W4387092334 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37756369" @default.
• W4387092334 hasPublicationYear "2023" @default.
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