ArticleNucleic acids research2023
ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 20 citations in OpenAlex.
- Systems biology framework for the rational design of operational conditions for in vitro/in vivo translation of tissue models.Science advances · 2026Article
- Accelerating Leigh syndrome drug discovery through deep learning screening in brain organoids.Nature communications · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- CRESTA: a comprehensive transcriptome atlas for cellular response to external stressors.Nucleic acids research · 2026Article
- Article
- Advancing active compound discovery for novel drug targets: insights from AI-driven approaches.Acta pharmacologica Sinica · 2025Review
- Towards an interpretable deep learning model of cancer.NPJ precision oncology · 2025Review
- Reversal gene expression assessment for drug repurposing, a case study of glioblastoma.Journal of translational medicine · 2025Article
- Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery.Nature communications · 2024Article
- Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3DBiophysics reviews · 2024Review
- PC3T: a signature-driven predictor of chemical compounds for cellular transition.Communications biology · 2023Article
- On the correspondence between the transcriptomic response of a compound and its effects on its targets.BMC bioinformatics · 2023Article
- Application of perturbation gene expression profiles in drug discovery-From mechanism of action to quantitative modelling.Frontiers in systems biology · 2023Review
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prior knowledge of perturbation data can significantly assist in inferring the relationship between chemical perturbations and their specific transcriptional response. However, current databases mostly contain cancer cell lines, which are unsuitable for the aforementioned inference in non-cancer cells, such as cells related to non-cancer disease, immunology and aging. Here, we present ChemPert (https://chempert.uni.lu/), a database consisting of 82 270 transcriptional signatures in response to 2566 unique perturbagens (drugs, small molecules and protein ligands) across 167 non-cancer cell types, as well as the protein targets of 57 818 perturbagens. In addition, we develop a computational tool that leverages the non-cancer cell datasets, which enables more accurate predictions of perturbation responses and drugs in non-cancer cells compared to those based onto cancer databases. In particular, ChemPert correctly predicted drug effects for treating hepatitis and novel drugs for osteoarthritis. The ChemPert web interface is user-friendly and allows easy access of the entire datasets and the computational tool, providing valuable resources for both experimental researchers who wish to find datasets relevant to their research and computational researchers who need comprehensive non-cancer perturbation transcriptomics datasets for developing novel algorithms. Overall, ChemPert will facilitate future in silico compound screening for non-cancer cells.
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