ArticleNPJ digital medicine2024
Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach.
Article in NPJ digital medicine, 2024. 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.
- ToxiGuard: an AOP-guided mechanistically interpretable framework for multi-organ toxicity prediction.Archives of toxicology · 2026Article
- GanCtrl: a generative AI approach to derive study-aligned synthetic controls for reducing concurrent control animal use.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- GANomics: bridging legacy and modern transcriptomic platforms for clinical applications.NPJ genomic medicine · 2026Article
- Building trust in the integration of artificial intelligence into chemical risk assessment: findings from the 2024 ECETOC workshop.Archives of toxicology · 2026Article
- Big Data for Drug Discovery: Some Historical Landscape, Considerations, and Applications for a Medicinal Chemist.ACS omega · 2026Review
- Evolution of artificial intelligence and machine learning in DILI toxicogenomics: from descriptive profiling to mechanistic insights.Frontiers in pharmacology · 2026Review
- Beyond QSARs: Quantitative Knowledge-Activity Relationships (QKARs) for enhanced drug toxicity prediction.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- The evolution of Alzheimer's target identification: Towards a fusion of artificial and cellular intelligence.The journal of prevention of Alzheimer's disease · 2025Article
- AIVIVE: a novel AI framework for enhanced in vitro to in vivo extrapolation (IVIVE) of toxicogenomics data.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- From big data to smart decisions: artificial intelligence in kidney risk assessment.Nature reviews. Nephrology · 2025Article
- Digital Twins for Personalized Medicine Require Epidemiological Data and Mathematical Modeling: Viewpoint.Journal of medical Internet research · 2025Article
- Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Translational research in toxicology has significantly benefited from transcriptomic profiling, particularly in drug safety. However, its application has predominantly focused on limited organs, notably the liver, due to resource constraints. This paper presents TransTox, an innovative AI model using a generative adversarial network (GAN) method to facilitate the bidirectional translation of transcriptomic profiles between the liver and kidney under drug treatment. TransTox demonstrates robust performance, validated across independent datasets and laboratories. First, the concordance between real experimental data and synthetic data generated by TransTox was demonstrated in characterizing toxicity mechanisms compared to real experimental settings. Second, TransTox proved valuable in gene expression predictive models, where synthetic data could be used to develop gene expression predictive models or serve as "digital twins" for diagnostic applications. The TransTox approach holds the potential for multi-organ toxicity assessment with AI and advancing the field of precision toxicology.
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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.