ArticleBioinformatics (Oxford, England)2025
Integrating population-level and cell-based signatures for drug repositioning.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- A functional genomics-pharmacotranscriptomics framework identifies host-directed anti-influenza agents.iScience · 2026Article
- An MHC-Related Gene's Signature Predicts Prognosis and Immune Microenvironment Infiltration in Glioblastoma.International journal of molecular sciences · 2025Article
- Transcriptome-Wide Association Studies (TWAS): Methodologies, Applications, and Challenges.Current protocols · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
motivationDrug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Drugs with human genetic evidence are more likely to advance successfully through clinical trials toward Food and Drug Administration approval. Single gene-based drug repositioning methods have been implemented, but approaches leveraging a broad spectrum of molecular signatures remain underexplored.
resultsWe propose a framework called "Transcriptome-informed Reversal Distance" (TReD) that embeds the disease signatures and drug response profiles into a high-dimensional normed space to quantify the reversal potential of candidate drugs in a disease-related cell-based screening. We applied TReD to COVID-19, type 2 diabetes, and Alzheimer's disease (AD), identifying 36, 16, and 11 candidate drugs, respectively. Among these, literature supports 69% (25/36), 31% (5/16), and 64% (7/11) of the drugs, with clinical trials conducted for seven COVID-19 candidates and three AD candidates. In summary, we propose a comprehensive genetics-anchored framework integrating population-level signatures and cell-based screening that has the potential to accelerate the search for new therapeutic strategies. AVAILABILITY AND IMPLEMENTATION: Source code and datasets considered in this study are available at Github (https://github.com/zdangm/TReD). An archived snapshot is deposited at Zenodo (https://doi.org/10.5281/zenodo.16791909).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.