ArticleNature communications2019
Mapping the perturbome network of cellular perturbations.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 60 citations in OpenAlex.
- In Silico Perturbome Analysis Reveals Conserved Genes and Drug-Target Interactions inPathogens (Basel, Switzerland) · 2026Article
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- Integrating AI, mechanistic modelling and network approaches in systems biology for translational research.NPJ systems biology and applications · 2026Article
- A network-based map of the chemical exposome connects molecular interactions to public health.Nature communications · 2026Article
- A phenotypicHuman reproduction open · 2026Article
- Harnessing transcriptomics for discovery of natural products to overcome acquired cancer resistance.Archives of pharmacal research · 2026Review
- Core Perturbomes ofPathogens (Basel, Switzerland) · 2025Article
- Scalable, compressed phenotypic screening using pooled perturbations.Nature biotechnology · 2025Article
- Optimizing drug synergy prediction through categorical embeddings in deep neural networks.Biology methods & protocols · 2025Article
- Cross-disease transcriptomic meta-analysis and network pharmacology reveal key therapeutic targets in rheumatoid arthritis, systemic lupus erythematosus and multiple sclerosis.Frontiers in bioinformatics · 2025Article
- AI-empowered perturbation proteomics for complex biological systems.Cell genomics · 2024Article
- Article
- Discovery of Molecular Glue Degraders via Isogenic Morphological Profiling.ACS chemical biology · 2023Article
- Constructing maps between distinct cell fates and parametric conditions by systematic perturbations.Bioinformatics (Oxford, England) · 2023Article
- AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation.Science advances · 2023Article
- Computational Construction of Toxicant Signaling Networks.Chemical research in toxicology · 2023Article
- Peripheral blood mononuclear cell phenotype and function are maintained after overnight shipping of whole blood.Scientific reports · 2022Article
- A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses.Communications biology · 2022Article
- Parallelization with Dual-Trap Single-Column Configuration Maximizes Throughput of Proteomic Analysis.Analytical chemistry · 2022Article
- Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic.PLoS computational biology · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug combinations provide effective treatments for diverse diseases, but also represent a major cause of adverse reactions. Currently there is no systematic understanding of how the complex cellular perturbations induced by different drugs influence each other. Here, we introduce a mathematical framework for classifying any interaction between perturbations with high-dimensional effects into 12 interaction types. We apply our framework to a large-scale imaging screen of cell morphology changes induced by diverse drugs and their combination, resulting in a perturbome network of 242 drugs and 1832 interactions. Our analysis of the chemical and biological features of the drugs reveals distinct molecular fingerprints for each interaction type. We find a direct link between drug similarities on the cell morphology level and the distance of their respective protein targets within the cellular interactome of molecular interactions. The interactome distance is also predictive for different types of drug interactions.
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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.