ArticleNature communications2021
Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Federated causal discovery in medicine: trends, opportunities, and challenges.Frontiers in digital health · 2026Article
- Genotype by Environment Interactions in Gene Regulation Underlie the Response to Soil Drying in the Model Grass Brachypodium distachyon.Molecular biology and evolution · 2025Article
- Drug-disease networks and drug repurposing.PLoS computational biology · 2025Article
- Image2Reg: Linking chromatin images to gene regulation using genetic and chemical perturbation screens.Cell systems · 2025Article
- Local patterns of genetic sharing between neuropsychiatric and insulin resistance-related conditions.Translational psychiatry · 2025Article
- DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer's disease.Scientific reports · 2025Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Article
- Identification of novel anti-ZIKV drugs from viral-infection temporal gene expression profiles.Emerging microbes & infections · 2023Article
- The benefits and pitfalls of machine learning for biomarker discovery.Cell and tissue research · 2023Review
- Transfer Learning with Kernel Methods.Nature communications · 2023Article
- Sex blind: bridging the gap between drug exposure and sex-related gene expression inFrontiers in toxicology · 2023Article
- Rational drug repositioning for coronavirus-associated diseases using directional mapping and side-effect inference.iScience · 2022Article
- Celastrol: A lead compound that inhibits SARS-CoV-2 replication, the activity of viral and human cysteine proteases, and virus-induced IL-6 secretion.Drug development research · 2022Article
- A review of causal discovery methods for molecular network analysis.Molecular genetics & genomic medicine · 2022Review
- SARS-CoV-2 Nsp6 damages Drosophila heart and mouse cardiomyocytes through MGA/MAX complex-mediated increased glycolysis.Communications biology · 2022Article
- Review
- Network approaches for modeling the effect of drugs and diseases.Briefings in bioinformatics · 2022Review
- Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2.Chemical reviews · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs.
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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.