ArticleBiology direct2021
An ensemble learning approach for modeling the systems biology of drug-induced injury.
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 13 papers.
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Who cites it
13 citing papers in PubMed, 13 citations in OpenAlex.
- Context-Aware Multilevel Classification of Semantic Relations in Drug-Adverse Drug Reaction (ADR) Networks-Predicting Drug-Induced Liver Injury (DILI) as a Case Study.Chemical research in toxicology · 2026Article
- Artificial Intelligence for Drug Safety Across the Lifecycle and Decision Type: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Evolution of artificial intelligence and machine learning in DILI toxicogenomics: from descriptive profiling to mechanistic insights.Frontiers in pharmacology · 2026Review
- Machine Learning to Predict Drug-Induced Liver Injury and Its Validation on Failed Drug Candidates in Development.Toxics · 2024Article
- Determining human-coronavirus protein-protein interaction using machine intelligence.Medicine in novel technology and devices · 2023Article
- State of the Art of Genomic Technology in Toxicology: A Review.International journal of molecular sciences · 2023Review
- TOXRIC: a comprehensive database of toxicological data and benchmarks.Nucleic acids research · 2023Article
- Artificial genetic polymers against human pathologies.Biology direct · 2022Review
- p53 regulates expression of nuclear envelope components in cancer cells.Biology direct · 2022Article
- An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning.Molecules (Basel, Switzerland) · 2022Article
- Prediction of Adverse Drug Reaction Linked to Protein Targets Using Network-Based Information and Machine Learning.Frontiers in bioinformatics · 2022Article
- The eTRANSAFE Project on Translational Safety Assessment through Integrative Knowledge Management: Achievements and Perspectives.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Predicting Drug-Induced Liver Injury Using Machine Learning on a Diverse Set of Predictors.Frontiers in pharmacology · 2021Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
Abstract
backgroundDrug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being one of the main causes of liver failure, the pathophysiology and mechanisms of DILI are poorly understood. In the present study, we developed an ensemble learning approach based on different features (CMap gene expression, chemical structures, drug targets) to predict drugs that might cause DILI and gain a better understanding of the mechanisms linked to the adverse reaction.
resultsWe searched for gene signatures in CMap gene expression data by using two approaches: phenotype-gene associations data from DisGeNET, and a non-parametric test comparing gene expression of DILI-Concern and No-DILI-Concern drugs (as per DILIrank definitions). The average accuracy of the classifiers in both approaches was 69%. We used chemical structures as features, obtaining an accuracy of 65%. The combination of both types of features produced an accuracy around 63%, but improved the independent hold-out test up to 67%. The use of drug-target associations as feature obtained the best accuracy (70%) in the independent hold-out test.
conclusionsWhen using CMap gene expression data, searching for a specific gene signature among the landmark genes improves the quality of the classifiers, but it is still limited by the intrinsic noise of the dataset. When using chemical structures as a feature, the structural diversity of the known DILI-causing drugs hampers the prediction, which is a similar problem as for the use of gene expression information. The combination of both features did not improve the quality of the classifiers but increased the robustness as shown on independent hold-out tests. The use of drug-target associations as feature improved the prediction, specially the specificity, and the results were comparable to previous research studies.
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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.