ArticleBiology direct2021
Integration of human cell lines gene expression and chemical properties of drugs for Drug Induced Liver Injury prediction.
Article in Biology direct, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Artificial intelligence in hepatopathy diagnosis and treatment: Big data analytics, deep learning, and clinical prediction models.World journal of gastroenterology · 2025Review
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions.Toxics · 2025Review
- Artificial genetic polymers against human pathologies.Biology direct · 2022Review
- FTY720 attenuates APAP‑induced liver injury via the JAK2/STAT3 signaling pathway.International journal of molecular medicine · 2022Article
- Robust Data Integration Method for Classification of Biomedical Data.Journal of medical systems · 2021Article
- Predicting Drug-Induced Liver Injury Using Machine Learning on a Diverse Set of Predictors.Frontiers in pharmacology · 2021Article
- Prediction of Alternative Drug-Induced Liver Injury Classifications Using Molecular Descriptors, Gene Expression Perturbation, and Toxicology Reports.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationDrug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI can bring a significant reduction in the cost of clinical trials. In this work we examined whether occurrence of DILI can be predicted using gene expression profile in cancer cell lines and chemical properties of drugs.
methodsWe used gene expression profiles from 13 human cell lines, as well as molecular properties of drugs to build Machine Learning models of DILI. To this end, we have used a robust cross-validated protocol based on feature selection and Random Forest algorithm. In this protocol we first identify the most informative variables and then use them to build predictive models. The models are first built using data from single cell lines, and chemical properties. Then they are integrated using Super Learner method with several underlying methods for integration. The entire modelling process is performed using nested cross-validation.
resultsWe have obtained weakly predictive ML models when using either molecular descriptors, or some individual cell lines (AUC ∈(0.55-0.61)). Models obtained with the Super Learner approach have a significantly improved accuracy (AUC=0.73), which allows to divide substances in two categories: low-risk and high-risk.
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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.