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Processed Data and Reproducible Analyses for Transcriptomics-Guided Computational Prioritization of CCR5 and NNMT Ligands in Bladder Cancer

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Zenodo2026-04-13 更新2026-05-26 收录
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https://zenodo.org/doi/10.5281/zenodo.18917294
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This Zenodo record contains processed data files, supplementary result tables, analysis scripts, docking validation files, machine-learning validation files, chemical-space support files, and environment information associated with the manuscript: “Transcriptomics-Guided Computational Prioritization of CCR5 and NNMT Ligands in Bladder Cancer: Docking Validation, Activity Modeling, and Chemical Space Analysis”. The archive is intended to support transparency and computational reproducibility of the analyses described in the manuscript. The archived analyses include:(1) resistance signature construction from public datasets,(2) projection of resistance signatures onto TCGA-BLCA transcriptomic profiles,(3) resistance-axis scoring and quadrant assignment,(4) Hallmark ssGSEA analysis,(5) effect size-based pathway and gene prioritization,(6) target-specific compound prioritization,(7) molecular docking,(8) docking protocol validation by redocking and retrospective benchmark analyses,(9) target-specific machine-learning validation including Y-scrambling for the CCR5 random forest model,(10) Pareto-based multi-objective prioritization, and(11) chemical-space visualization. Public source datasets analyzed in the study are available from the Genomic Data Commons (TCGA-BLCA), the Gene Expression Omnibus (GSE231835), and the IMvigor210CoreBiologies resource. CCR5 prioritization in this archive is based on an internally validated target-specific random forest workflow combined with docking analyses supported by redocking and retrospective benchmark evaluation. NNMT prioritization is retained as an exploratory target context based on archived screening scores from the original NNMT branch of the study. The record includes processed datasets, supplementary tables, source data underlying the main figures, analysis scripts, docking validation files, machine-learning validation files, chemical-space support files, and environment information required to reproduce the analyses reported in the manuscript.
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Zenodo
创建时间:
2026-03-16
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