M
Minjun Chen Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
High Impact
Researcher
faculty
42 h-index
168 pubs
5,627 cited
Research Areas
Biography and Research Information
OverviewAI-generated summary
Minjun Chen's research focuses on the prediction and assessment of drug-induced liver injury (DILI) in humans, utilizing computational modeling, machine learning, and preclinical assessment methods. Chen has investigated the performance of preclinical models in predicting DILI and has explored the association between specific clinical indicators, such as elevated bilirubin and alkaline phosphatase, and the duration of recovery from DILI. Their work also involves applying natural language processing techniques, such as BERT, to drug labeling documents to classify DILI risk.
Further research includes the development and validation of computational models, particularly those employing deep learning and machine learning, for predicting liver toxicity. Chen has also examined alternative in silico methods for predicting drug and herb-induced liver injury. Collaborations include extensive work with Tsung‐Jen Liao, Kristin Ashby, Tucker A. Patterson, and Weida Tong, all from the National Center for Toxicological Research, contributing to a significant number of shared publications.
With an h-index of 43 and over 5,600 citations across 169 publications, Chen is recognized as a highly cited researcher. Their work contributes to the field of toxicology and pharmaceutical safety, aiming to improve the prediction of adverse drug reactions and enhance drug development processes.
Metrics
- h-index: 42
- Publications: 168
- Citations: 5,627
Selected Publications
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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)
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New Alternative Methods in Drug Safety Assessment (2023)
Collaboration Network
Top Collaborators
Weida Tong
National Center for Toxicological Research (US)
12 shared publications
- 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
- A systematic comparison of hepatobiliary adverse drug reactions in FDA and EMA drug labeling reveals discrepancies
Showing 5 of 12 shared publications
Tsung‐Jen Liao
9 shared publications
In database
- 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 9 shared publications
Huixiao Hong
National Center for Toxicological Research
7 shared publications
In database
- 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
Kristin Ashby
National Center for Toxicological Research
5 shared publications
In database
- 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
Yue Wu
National Center for Toxicological Research (US)
5 shared publications
- 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
- A systematic comparison of hepatobiliary adverse drug reactions in FDA and EMA drug labeling reveals discrepancies
- 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
Ismael Álvarez‐Álvarez
Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (ES)
4 shared publications
- 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
- Evaluation of the role of comedication properties in the severity of drug-induced liver injury using machine learning techniques
Raúl J. Andrade
Universidad de Málaga (ES)
4 shared publications
- 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
- Evaluation of the role of comedication properties in the severity of drug-induced liver injury using machine learning techniques
M. Isabel Lucena
Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (ES)
4 shared publications
- 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
- Evaluation of the role of comedication properties in the severity of drug-induced liver injury using machine learning techniques
Dongying Li
National Center for Toxicological Research
4 shared publications
In database
- 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
Fahad Mostafa
National Center for Toxicological Research (US)
4 shared publications
- 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
Yunjia Wang
4 shared publications
- β-catenin is a potential prognostic biomarker in uterine sarcoma
- Comprehensive Analysis of Non-coding RNAs Expression Profile and miRNA- circRNA-gene Co-expression Network in Developing Uterine Carcinosarcoma
- Author response for "Comprehensive Analysis of Non-coding RNAs Expression Profile and miRNA- circRNA-gene Co-expression Network in Developing Uterine Carcinosarcoma"
- Author response for "Comprehensive Analysis of Non-coding RNAs Expression Profile and miRNA- circRNA-gene Co-expression Network in Developing Uterine Carcinosarcoma"
Yue Huang
4 shared publications
- β-catenin is a potential prognostic biomarker in uterine sarcoma
- Comprehensive Analysis of Non-coding RNAs Expression Profile and miRNA- circRNA-gene Co-expression Network in Developing Uterine Carcinosarcoma
- Author response for "Comprehensive Analysis of Non-coding RNAs Expression Profile and miRNA- circRNA-gene Co-expression Network in Developing Uterine Carcinosarcoma"
- Author response for "Comprehensive Analysis of Non-coding RNAs Expression Profile and miRNA- circRNA-gene Co-expression Network in Developing Uterine Carcinosarcoma"
Chi Zhang
4 shared publications
- β-catenin is a potential prognostic biomarker in uterine sarcoma
- Comprehensive Analysis of Non-coding RNAs Expression Profile and miRNA- circRNA-gene Co-expression Network in Developing Uterine Carcinosarcoma
- Author response for "Comprehensive Analysis of Non-coding RNAs Expression Profile and miRNA- circRNA-gene Co-expression Network in Developing Uterine Carcinosarcoma"
- Author response for "Comprehensive Analysis of Non-coding RNAs Expression Profile and miRNA- circRNA-gene Co-expression Network in Developing Uterine Carcinosarcoma"
Su‐Han Jin
4 shared publications
- β-catenin is a potential prognostic biomarker in uterine sarcoma
- Comprehensive Analysis of Non-coding RNAs Expression Profile and miRNA- circRNA-gene Co-expression Network in Developing Uterine Carcinosarcoma
- Author response for "Comprehensive Analysis of Non-coding RNAs Expression Profile and miRNA- circRNA-gene Co-expression Network in Developing Uterine Carcinosarcoma"
- Author response for "Comprehensive Analysis of Non-coding RNAs Expression Profile and miRNA- circRNA-gene Co-expression Network in Developing Uterine Carcinosarcoma"
Benjamin Frey
4 shared publications
- β-catenin is a potential prognostic biomarker in uterine sarcoma
- Comprehensive Analysis of Non-coding RNAs Expression Profile and miRNA- circRNA-gene Co-expression Network in Developing Uterine Carcinosarcoma
- Author response for "Comprehensive Analysis of Non-coding RNAs Expression Profile and miRNA- circRNA-gene Co-expression Network in Developing Uterine Carcinosarcoma"
- Author response for "Comprehensive Analysis of Non-coding RNAs Expression Profile and miRNA- circRNA-gene Co-expression Network in Developing Uterine Carcinosarcoma"
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