Minjun Chen
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
Also affiliated: United States Food and Drug Administration (2003–2025); Sun Yat-sen University (2025); Shanxi University (2015–2018); Zunyi Medical University (2024); Shanghai Jiao Tong University (2007–2014); Chinese Academy of Sciences (2018); Chinese Academy of Medical Sciences & Peking Union Medical College (1998–2025); Peking University (2007–2012); National Medical Products Administration (2014); Peking Union Medical College Hospital (1998–2025); Chinese PLA General Hospital (2009); University of Belgrade (2014); Resonance Health (Australia) (2022); Cancer Research And Biostatistics (2024); Culture Resource (2016); Prince of Wales Hospital (2006); Guangzhou First People's Hospital (2012–2025); Peking University People's Hospital (2012); Shanghai Institute of Planned Parenthood Research (2008); The First Affiliated Hospital, Sun Yat-sen University (2025); Peking University Third Hospital (2007); Shanxi University of Traditional Chinese Medicine (2016); Research Center for Eco-Environmental Sciences (2018); Food and Drug Administration (2025); Tianjin Medical University (2008); South China University of Technology (2019); Guangzhou Medical University (2012–2025)
Faculty Researcher
Research Areas
Biomedical Subjects
Biography and Research Information
OverviewAI-generated summary
Minjun Chen's research focuses on computational and in silico methods for predicting drug-induced liver injury (DILI) and general hepatotoxicity. This work involves the application of machine learning and deep learning techniques to analyze complex biological data and drug information. Chen has investigated the performance of preclinical models in predicting DILI in humans, contributing to systematic reviews on the subject.
Further research includes the development and application of natural language processing (NLP) for analyzing drug labeling documents to classify DILI risk. Chen also explores the use of physiological liver microtissue systems for preclinical hepatotoxicity assessment and computational models for predicting liver toxicity in the context of deep learning advancements. The researcher's work is supported by a strong publication record in peer-reviewed journals and scholarship metrics including an h-index of 42 and over 5,600 citations.
Chen collaborates with researchers at the National Center for Toxicological Research, including Tsung-Jen Liao, Kristin Ashby, Tucker A. Patterson, and Weida Tong, on multiple shared publications. The researcher leads a group and maintains an active laboratory website, indicating ongoing research activities and mentorship.
Metrics
- h-index: 42
- Publications: 169
- Citations: 5,747
Selected Publications
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Evolution of artificial intelligence and machine learning in DILI toxicogenomics: from descriptive profiling to mechanistic insights (2026)
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DILIrank 2.0: An updated and expanded database for drug-induced liver injury risk based on FDA labeling and a literature review (2025)
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Machine learning and artificial intelligence methods for predicting liver toxicity (2025)
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Contributors (2025)
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New approach methodologies (NAMs) for drug-induced liver injury (DILI): Where are we now? (2025)
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Artificial Intelligence: An Emerging Tool for Studying Drug‐Induced Liver Injury (2025)
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Genetic Variants of <i>GBP4</i>: Reduced Risks for Drug‐Induced Acute Liver Failure in Non‐Finnish European Population (2025)
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Physiological liver microtissue 384-well microplate system for preclinical hepatotoxicity assessment of therapeutic small molecule drugs (2024)
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Drug interaction with UDP-Glucuronosyltransferase (UGT) enzymes is a predictor of drug-induced liver injury (2024)
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Automatic text classification of drug-induced liver injury using document-term matrix and XGBoost (2024)
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Machine Learning to Predict Drug-Induced Liver Injury and Its Validation on Failed Drug Candidates in Development (2024)
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Composite Plot for Visualizing Aminotransferase and Bilirubin Changes in Clinical Trials of Subjects with Abnormal Baseline Values (2024)
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Machine Learning to Predict Drug-Induced Liver Injury and its Validation on Failed Drug Candidates in Development (2024)
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Medical device report analyses from MAUDE: Device and patient outcomes, adverse events, and sex-based differential effects (2024)
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Computational models for predicting liver toxicity in the deep learning era (2024)
Collaboration Network
Top Collaborators
- BERT-Based Natural Language Processing of Drug Labeling Documents: A Case Study for Classifying Drug-Induced Liver Injury Risk
- Physiological liver microtissue 384-well microplate system for preclinical hepatotoxicity assessment of therapeutic small molecule drugs
- Machine Learning Models for Predicting Liver Toxicity
- Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose
- Integrative approaches for studying the role of noncoding RNAs in influencing drug efficacy and toxicity
Showing 5 of 11 shared publications
- Drug interaction with UDP-Glucuronosyltransferase (UGT) enzymes is a predictor of drug-induced liver injury
- Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced Liver Injury
- Medical device report analyses from MAUDE: Device and patient outcomes, adverse events, and sex-based differential effects
- Whole Exome Sequencing Reveals Genetic Variants in HLA Class II Genes Associated With Transplant-free Survival of Indeterminate Acute Liver Failure
- Computational Modeling for the Prediction of Hepatotoxicity Caused by Drugs and Chemicals
Showing 5 of 8 shared publications
- Machine Learning Models for Predicting Liver Toxicity
- Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose
- Integrative approaches for studying the role of noncoding RNAs in influencing drug efficacy and toxicity
- Whole Exome Sequencing Reveals Genetic Variants in HLA Class II Genes Associated With Transplant-free Survival of Indeterminate Acute Liver Failure
- Computational Modeling for the Prediction of Hepatotoxicity Caused by Drugs and Chemicals
Showing 5 of 7 shared publications
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced Liver Injury
- Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach
- Computational Modeling for the Prediction of Hepatotoxicity Caused by Drugs and Chemicals
- Transporter, Drug Metabolism, and Drug‐Induced Liver Injury in Marketed Drugs
- BERT-Based Natural Language Processing of Drug Labeling Documents: A Case Study for Classifying Drug-Induced Liver Injury Risk
- Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose
- Automatic text classification of drug-induced liver injury using document-term matrix and XGBoost
- Transporter, Drug Metabolism, and Drug‐Induced Liver Injury in Marketed Drugs
- Integrative approaches for studying the role of noncoding RNAs in influencing drug efficacy and toxicity
- New approach methodologies (NAMs) for drug-induced liver injury (DILI): Where are we now?
- DILIrank dataset for QSAR modeling of drug-induced liver injury
- DILIrank 2.0: An updated and expanded database for drug-induced liver injury risk based on FDA labeling and a literature review
- Computational models for predicting liver toxicity in the deep learning era
- Machine Learning to Predict Drug-Induced Liver Injury and Its Validation on Failed Drug Candidates in Development
- Machine Learning to Predict Drug-Induced Liver Injury and its Validation on Failed Drug Candidates in Development
- Machine learning and artificial intelligence methods for predicting liver toxicity
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- Artificial Intelligence: An Emerging Tool for Studying Drug‐Induced Liver Injury
- Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach
- Genetic Variants of <i>GBP4</i>: Reduced Risks for Drug‐Induced Acute Liver Failure in Non‐Finnish European Population
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach
- Genetic Variants of <i>GBP4</i>: Reduced Risks for Drug‐Induced Acute Liver Failure in Non‐Finnish European Population
- Machine Learning Models for Predicting Liver Toxicity
- Computational Modeling for the Prediction of Hepatotoxicity Caused by Drugs and Chemicals
- Decision forest—a machine learning algorithm for QSAR modeling
- Whole Exome Sequencing Reveals Genetic Variants in HLA Class II Genes Associated With Transplant-free Survival of Indeterminate Acute Liver Failure
- Composite Plot for Visualizing Aminotransferase and Bilirubin Changes in Clinical Trials of Subjects with Abnormal Baseline Values
- Genetic Variants of <i>GBP4</i>: Reduced Risks for Drug‐Induced Acute Liver Failure in Non‐Finnish European Population
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- Genetic Variants of <i>GBP4</i>: Reduced Risks for Drug‐Induced Acute Liver Failure in Non‐Finnish European Population
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