ReviewFrontiers in systems biology2023
Application of perturbation gene expression profiles in drug discovery-From mechanism of action to quantitative modelling.
Review in Frontiers in systems biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Predicted brain-regional gene expression patterns in individuals living with Alzheimer's disease.Neurobiology of aging · 2026Article
- Article
- CFM-GP: unified conditional flow matching to learn gene perturbation across cell types.NAR genomics and bioinformatics · 2026Article
- DRIVE: a comprehensive resource deciphering drug-induced transcriptomic and splicing response in cancer cell.Neoplasia (New York, N.Y.) · 2026Article
- The multifaceted metabolite landscape of gut microbiota: systems pharmacology insights into Crohn's disease, irritable bowel disease, and ulcerative colitis.Molecular diversity · 2026Article
- Predicting condition-aware drug-induced transcriptional responses via a latent diffusion model.Bioinformatics (Oxford, England) · 2026Article
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
- Integrating artificial intelligence across the cancer drug discovery pipeline using a design-test-refine workflow.Frontiers in oncology · 2026Review
- A phenotypicHuman reproduction open · 2026Article
- Harnessing transcriptomics for discovery of natural products to overcome acquired cancer resistance.Archives of pharmacal research · 2026Review
- Article
- GSFM: A genome-scale functional module transformation to represent drug efficacy forActa pharmaceutica Sinica. B · 2025Article
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2 authors.
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Abstract
High dimensional characterization of drug targets, compound effects and disease phenotypes are crucial for increased efficiency of drug discovery. High-throughput gene expression measurements are one of the most frequently used data acquisition methods for such a systems level analysis of biological phenotypes. RNA sequencing allows genome wide quantification of transcript abundances, recently even on the level of single cells. However, the correct, mechanistic interpretation of transcriptomic measurements is complicated by the fact that gene expression changes can be both the cause and the consequence of altered phenotype. Perturbation gene expression profiles, where gene expression is measured after a genetic or chemical perturbation, can help to overcome these problems by directly connecting the causal perturbations to their gene expression consequences. In this Review, we discuss the main large scale perturbation gene expression profile datasets, and their application in the drug discovery process, covering mechanisms of action identification, drug repurposing, pathway activity analysis and quantitative modelling.
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