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Confirmed
Presence
Current · Arkansas
Last published
2025
Sources
OpenAlex · ORCID
Refreshed
2026-10-06
M
Minjun Chen
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
High Impact
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Also affiliated: United States Food and Drug Administration (2003–2025); 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 (2012); Peking Union Medical College Hospital (1998–2025); Chinese PLA General Hospital (2009); University of Belgrade (2014); Resonance Health (Australia) (2022); 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); National Institutes for Food and Drug Control (2014); Peking University Third Hospital (2007); Research Center for Eco-Environmental Sciences (2018); State Key Laboratory of Environmental Chemistry and Ecotoxicology (2018); Tianjin Medical University (2008); South China University of Technology (2019)
42 h-index
166 pubs
5,825 cited
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: 166
- Citations: 5,825
Positions
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National Center for Toxicological Research 2008–presentORCID
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Postdoctoral fellow 2006–2008University of Medicine and Dentistry of New Jersey ORCID
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Assistant and associate professor 2003–2007Shanghai jiaotong university School of Pharmacy ORCID
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 GBP4: 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
Weida Tong
National Center for Toxicological Research
46 shared publications
In database
- The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
- FDA-approved drug labeling for the study of drug-induced liver injury
- DILIrank: the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans
- High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury
- A Decade of Toxicogenomic Research and Its Contribution to Toxicological Science
Showing 5 of 46 shared publications
Huixiao Hong
National Center for Toxicological Research
20 shared publications
In database
- The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
- Predicting Hepatotoxicity Using ToxCastin VitroBioactivity and Chemical Structure
- A review on machine learning methods forin silicotoxicity prediction
- Toward Predictive Models for Drug-Induced Liver Injury in Humans: Are we There Yet?
- Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs
Showing 5 of 20 shared publications
Jürgen Borlak
Medizinische Hochschule Hannover (DE)
16 shared publications
- Drug-induced liver injury: Interactions between drug properties and host factors
- High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury
- A Decade of Toxicogenomic Research and Its Contribution to Toxicological Science
- Toward Predictive Models for Drug-Induced Liver Injury in Humans: Are we There Yet?
- A Model to predict severity of drug‐induced liver injury in humans
Showing 5 of 16 shared publications
Hong Fang
ICF International (United States)
13 shared publications
In database
- FDA-approved drug labeling for the study of drug-induced liver injury
- Toward Predictive Models for Drug-Induced Liver Injury in Humans: Are we There Yet?
- ArrayTrack: An FDA and Public Genomic Tool
- A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model
- The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury
Showing 5 of 13 shared publications
Ayako Suzuki
Duke University (US)
10 shared publications
- Drug-induced liver injury: Interactions between drug properties and host factors
- DILIrank: the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans
- Interplay of gender, age and drug properties on reporting frequency of drug-induced liver injury
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- Mining hidden knowledge for drug safety assessment: topic modeling of LiverTox as a case study
Showing 5 of 10 shared publications
Kristin Ashby
National Center for Toxicological Research
8 shared publications
In database
- Review article: therapeutic bile acids and the risks for hepatotoxicity
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- The Development of a Database for Herbal and Dietary Supplement Induced Liver Toxicity
- 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
Showing 5 of 8 shared publications
Tsung-Jen Liao
National Institutes of Health
8 shared publications
In database
- Drug interaction with UDP-Glucuronosyltransferase (UGT) enzymes is a predictor of drug-induced liver injury
- Medical device report analyses from MAUDE: Device and patient outcomes, adverse events, and sex-based differential effects
- Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced Liver Injury
- 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
Raúl J. Andrade
Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (ES)
6 shared publications
- Drug-induced liver injury: Interactions between drug properties and host factors
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- The influence of drug properties and host factors on delayed onset of symptoms in 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
- Genetic Variants of GBP4: Reduced Risks for Drug‐Induced Acute Liver Failure in Non‐Finnish European Population
Showing 5 of 6 shared publications
M. Isabel Lucena
Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (ES)
6 shared publications
- Drug-induced liver injury: Interactions between drug properties and host factors
- Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI
- The influence of drug properties and host factors on delayed onset of symptoms in 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
- Genetic Variants of GBP4: Reduced Risks for Drug‐Induced Acute Liver Failure in Non‐Finnish European Population
Showing 5 of 6 shared publications
Jieqiang Zhu
National Center for Toxicological Research (US)
6 shared publications
- The Development of a Database for Herbal and Dietary Supplement Induced Liver Toxicity
- Quantitative Structure–Activity Relationship Models for Predicting Risk of Drug-Induced Liver Injury in Humans
- The landscape of hepatobiliary adverse reactions across 53 herbal and dietary supplements reveals immune-mediated injury as a common cause of hepatitis
- Cancer genomics predicts disease relapse and therapeutic response to neoadjuvant chemotherapy of hormone sensitive breast cancers
- Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose
Showing 5 of 6 shared publications
Chaoyang Zhang
University of Southern Mississippi (US)
6 shared publications
- A review on machine learning methods forin silicotoxicity prediction
- Quantitative Structure–Activity Relationship Models for Predicting Risk of Drug-Induced Liver Injury in Humans
- A Review of Feature Reduction Methods for QSAR-Based Toxicity Prediction
- Target-specific toxicity knowledgebase (TsTKb): a novel toolkit for in silico predictive toxicology
- Editorial: Deep Learning for Toxicity and Disease Prediction
Showing 5 of 6 shared publications
Stephen Harris
National Center for Toxicological Research (US)
5 shared publications
- The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
- ArrayTrack: An FDA and Public Genomic Tool
- atBioNet– an integrated network analysis tool for genomics and biomarker discovery
- A Unifying Ontology to Integrate Histological and Clinical Observations for Drug-Induced Liver Injury
- ArrayTrack: An FDA and Public Genomic Tool
Jie Liu
National Center for Toxicological Research
5 shared publications
In database
- The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
- Predicting Hepatotoxicity Using ToxCastin VitroBioactivity and Chemical Structure
- 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
Zhichao Liu
National Center for Toxicological Research (US)
5 shared publications
- The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
- FDA-approved drug labeling for the study of drug-induced liver injury
- A Unifying Ontology to Integrate Histological and Clinical Observations for Drug-Induced Liver Injury
- NETBAGs: A Network-Based Clustering Approach with Gene Signatures for Cancer Subtyping Analysis
- Predicting the Risks of Drug-Induced Liver Injury in Humans Utilizing Computational Modeling
Qiang Shi
National Center for Toxicological Research
5 shared publications
In database
- The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
- FDA-approved drug labeling for the study of drug-induced liver injury
- A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model
- Unravelling Sex Differences in Drug‐Induced Liver Injury
- New Alternative Methods in Drug Safety Assessment
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