ArticleNature communications2017
A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed.
- TechBio 3.0 closes the drug discovery loop with multimodal AI and generative chemistry.npj drug discovery · 2026Review
- Machine Learning-Based Models to Predict Drug-Induced Liver Injury (DILI) to Assist Medicinal Chemistry.Journal of medicinal chemistry · 2026Review
- Evolution of artificial intelligence and machine learning in DILI toxicogenomics: from descriptive profiling to mechanistic insights.Frontiers in pharmacology · 2026Review
- Apoptotic signatures allow early and rapid screening of drug-induced liver injury to accelerate drug discovery.Communications medicine · 2025Article
- A large-scale human toxicogenomics resource for drug-induced liver injury prediction.Nature communications · 2025Article
- Development of an AI model for DILI-level prediction using liver organoid brightfield images.Communications biology · 2025Article
- High-throughput screening data generation, scoring and FAIRification: a case study on nanomaterials.Journal of cheminformatics · 2025Article
- Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features.Acta pharmaceutica Sinica. B · 2025Article
- Progress in toxicogenomics to protect human health.Nature reviews. Genetics · 2025Review
- The computationally guided design of selective targeters of multiple proteins (STaMPs) as a new opportunity for small molecule drug discovery.Frontiers in pharmacology · 2025Review
- Deep Learning Prediction of Drug-Induced Liver Toxicity by Manifold Embedding of Quantum Information of Drug Molecules.Pharmaceutical research · 2025Article
- Advances in drug-induced liver injury research: in vitro models, mechanisms, omics and gene modulation techniques.Cell & bioscience · 2024Review
- Feed Components and Timing to Improve the Feed Conversion Ratio for Sustainable Aquaculture Using Starch.International journal of molecular sciences · 2024Article
- Probing Liver Injuries Induced by Thioacetamide in Human In Vitro Pooled Hepatocyte Experiments.International journal of molecular sciences · 2024Article
- A network-based transcriptomic landscape of HepG2 cells uncovering causal gene-cytotoxicity interactions underlying drug-induced liver injury.Toxicological sciences : an official journal of the Society of Toxicology · 2024Article
- In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review.Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association · 2023Review
- New approach methodologies in human regulatory toxicology - Not if, but how and when!Environment international · 2023Review
- PROTEOMAS: a workflow enabling harmonized proteomic meta-analysis and proteomic signature mapping.Journal of cheminformatics · 2023Article
- Mechanistic Insights into the Biological Effects of Engineered Nanomaterials: A Focus on Gold Nanoparticles.International journal of molecular sciences · 2023Review
- Expanding RNAi therapeutics to extrahepatic tissues with lipophilic conjugates.Nature biotechnology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a 'big data compacting and data fusion'-concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a 'predictive toxicogenomics space' (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 10
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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.